Všechny publikace
A Novel Dataset for Streaming Learning Analytics
This research introduces a novel dataset developed for streaming learning analytics, derived from the Open University Learning Analytics Dataset (OULAD). The dataset incorporates essential temporal in…
This research introduces a novel dataset developed for streaming learning analytics, derived from the Open University Learning Analytics Dataset (OULAD). The dataset incorporates essential temporal information that captures the timing of student interactions with the Virtual Learning Environment (VLE). By integrating these time-based interactions, the dataset enhances the capabilities of stream algorithms, which are particularly well-suited for real-time monitoring and analysis of student learning behaviors. The dataset consists of 34 features and 1,718,983 samples, encompassing students' demographic information, assessment scores, and interactions with the VLE for a specific time ( T ), corresponding to each student ( S ) within a given course ( C ) and module ( M ). The target classes—'Withdrawn', 'Fail', 'Pass', and 'Distinction'—were encoded as 0, 1, 2, and 3, respectively. Notably, the data exhibits a significant imbalance, with a substantial prevalence of records associated with students who passed the final examination. The class distribution is as follows: 'Pass' (1,022,760 samples), 'Distinction' (308,642 samples), 'Fail' (227,550$ samples), and 'Withdrawn' (160,031 samples).
The fusion of hyperparameter candidates for one-class classification problems
Refining effect size measures and classification for differential item functioning: Toward unified guidelines across methods
Differential Item Functioning (DIF) analysis is used to identify potentially biased items in multi-item measurements. In addition to testing the statistical significance, it is essential to evaluate t…
Differential Item Functioning (DIF) analysis is used to identify potentially biased items in multi-item measurements. In addition to testing the statistical significance, it is essential to evaluate the practical significance of DIF through effect size measures. We review existing DIF effect size measures and cut-off values used to classify the effect size magnitudes for the Mantel-Haenszel test, SIBTEST, and logistic regression for binary items, and introduce a refinement of area-based effect size measures. A simulation study is conducted to investigate the properties of these effect size measures and existing classification guidelines, and to assess their comparative performance. The results indicate that some commonly used effect size measures exhibit undesirable properties, including inconsistent classifications, systematic underestimation of the magnitude of the underlying DIF, and strong dependence on design factors. To address these issues, we introduce usage restrictions for some effect size measures, revised cut-off values that unify results across different methods, and propose new cut-off values for area-based effect size measures. The methods are demonstrated using two real data examples. Implementation is provided in the R software.
Physical Activity and Life Satisfaction Data from the 4HAIE Study for Modelling Nonlinear Dynamics
This repository contains the dataset and accompanying code used in the manuscript: "Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data." …
This repository contains the dataset and accompanying code used in the manuscript: "Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data." The dataset originates from the 4HAIE (Healthy Aging in Industrial Environment - Program 4) study, a 12-month intensive longitudinal study designed to examine within-person dynamics of health behaviors and well-being in everyday life (see corresponding protocol paper Elavsky et al., 2021). Dataset Contents The repository includes: Merged analysis dataset linking daily physical activity with corresponding end-of-day life satisfaction assessments. Daily physical activity data collected continuously over approximately 12 months using Fitbit Charge 3 and Fitbit Charge 4 devices. Physical activity is represented by daily step counts. Ecological Momentary Assessment (EMA) data collected during four 14-day measurement bursts distributed across the 12-month study period. Participants completed an end-of-day survey assessing life satisfaction using two items adapted from the Satisfaction with Life Scale (SWLS). Illustrative analysis code demonstrating the generalized additive modeling (GAM) approach used to investigate nonlinear relationships between physical activity and life satisfaction over time. Study Design Participants were monitored continuously for physical activity throughout the study while completing repeated intensive EMA assessments during four separate two-week measurement bursts. This design enabled examination of both long-term behavioral patterns and short-term fluctuations in subjective well-being within individuals. Variables The repository includes variables related to: Participant identifier (de-identified) Date of observation Daily step count (Fitbit) End-of-day life satisfaction ratings (two EMA items) Additional temporal variables required for longitudinal modeling Variable definitions and coding information are provided in the accompanying documentation. Purpose The dataset is intended to illustrate statistical approaches for analyzing intensive longitudinal health behavior data, with particular emphasis on nonlinear modeling of within-person associations using generalized additive models (GAMs). Rather than serving solely as an empirical example, the dataset demonstrates a methodological framework for capturing complex temporal relationships that may not be adequately represented using traditional linear approaches. Associated Publication This dataset accompanies the manuscript: Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data. If you use this dataset or the accompanying analysis code, please cite both this Zenodo repository and the associated publication. References: ELavsky, S., Brabec, M., Maly, M. (in review). Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data. Elavsky S, Jandačková V, Knapová L, Vašendová V, Sebera M, Kaštovská B, Blaschová D, Kühnová J, Cimler R, Vilímek D, Bosek T, Koenig J, Jandačka D. Physical activity in an air-polluted environment: behavioral, psychological and neuroimaging protocol for a prospective cohort study (Healthy Aging in Industrial Environment study - Program 4). BMC Public Health. 2021 Jan 12;21(1):126. doi: 10.1186/s12889-021-10166-4. PMID: 33435943; PMCID: PMC7801866. Elavsky S, Brabec M, Maly M, Knapova L, Kastovska B, Sebera M, Ely M, Jandackova VK, Keller J, Pavel M. The temporal dynamics of the association between daily physical activity and life satisfaction. Ann Behav Med. 2025 Jan 4;59(1):kaaf079. doi: 10.1093/abm/kaaf079. PMID: 41224246; PMCID: PMC12757008.
Pilot Assessment of Transparency of LLM-based Systems to Support Emergency Rooms
One of the main challenges when developing medical decision support systems for the emergency room is adequately filtering the most relevant information. High workload, stress, and the necessity for u…
One of the main challenges when developing medical decision support systems for the emergency room is adequately filtering the most relevant information. High workload, stress, and the necessity for urgent decisions require precise answers to the questions posed. Although LLM-based systems can provide abundant information, physicians need concise and relevant data in this particular clinical setting. In this study, we perform a pilot assessment of the transparency of selected LLM-based systems. The comparative analysis includes ChatGPT o1 model, which was asked to produce responses with varying temperatures and a pilot graph-based RAG specializing in cardiovascular diseases. A survey was conducted among 33 clinicians regarding the amount of information contained in the provided prompts. Physicians favored the most readable, specific, and helpful answers in emergency department conditions. Reliable medical data and the form in which answers are delivered are crucial for physicians working in the emergency room. We conclude that physicians have preferences for LLM responses at a specific temperature. Further research should be expanded to enable tailoring responses not only to the clinical situation but also to the experience of the asking physician.
Positive body image is a pathway between nature contact and life satisfaction across 58 nations
Size-free one-class classification approach using probabilistic local cluster balance
Evolving fuzzy classification for human-centered explainable learning analytics in virtual environments
This study explores the use of explainable artificial intelligence in education, with a focus on its relevance for Learning Analytics. The research introduces a prototype-based dynamic incremental cla…
This study explores the use of explainable artificial intelligence in education, with a focus on its relevance for Learning Analytics. The research introduces a prototype-based dynamic incremental classification algorithm, Dynamic Incremental Semi-Supervised Fuzzy C-Means (DISSFCM), which leverages fuzzy logic to analyze student interaction data from virtual learning platforms, even when the data are only partially labeled. The proposed methodology generates human-centered explanations by extracting IF-THEN fuzzy rules from the evolving prototypes produced by DISSFCM over successive time intervals. These explanations, expressed in linguistic terms, remain accessible to non-expert stakeholders and are particularly suitable for educational contexts. The Open University Learning Analytics Dataset (OULAD) is utilized for experimentation and validation, providing a realistic scenario for semi-supervised data collection. Visual summaries of the evolving fuzzy rules support the identification of temporal patterns in streaming data. Results show that the model effectively adapts to concept drift while maintaining interpretability. Most notably, it proves robust in handling partially labeled data and variable time granularities, two challenges frequently encountered in real-world Learning Analytics sce- narios. The ability to both predict student outcomes and provide intelligible explanations under such constraints highlights the practical value of the approach. To evaluate the quality and relevance of the generated explanations, an expert-based evaluation was conducted. Domain experts evaluated the clarity, usefulness, and accuracy of the explanations in terms of their support for human understanding and decision-making. The results suggest that the explanations were perceived as generally informative and useful, supporting the method’s relevance for human-centered educational applications.
Incremental learning and granular computing from evolving data streams: An application to speech-based bipolar disorder diagnosis
We apply an evolving granular-computing modeling approach, called evolving Optimal Granular System (eOGS), to bipolar mood disorder (BD) diagnosis based on speech data streams. The eOGS online learnin…
We apply an evolving granular-computing modeling approach, called evolving Optimal Granular System (eOGS), to bipolar mood disorder (BD) diagnosis based on speech data streams. The eOGS online learning algorithm reveals information granules in the flow and design the structure and parameters of a granular rule-based model with a certain degree of interpretability based on acoustic attributes obtained from phone calls made over 7 months to the Psychiatry department of a hospital. A multi-objective programming problem that trades-off information specificity, model compactness, and numerical and granular error indices is presented. Spectral and prosodic attributes are ranked and selected based on a hybrid Pearson-Spearman correlation coefficient. Low attribute-class correlation, ranging from 0.03 to 0.07, is observed, as well as high class overlap, which is typical in the psychiatric field. eOGS models for BD recognition overcome alternative computational-intelligence models, namely, Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) and Fuzzy-set-Based evolving Modeling (FBeM-Gauss), by a small margin in both best and average cases; followed by eXtended Takagi-Sugeno (xTS) and evolving Takagi Sugeno (eTS) types of models. The proposed eOGS model using only 8 of the original acoustic attributes, and about 15 ‘If-Then’ inference rules, has exhibited the best root mean square error, 0.1361, and 91.8% accuracy in sharp BD class estimates. Granules associated to linguistic labels and a granular input-output map offer human understandability with relation to the inherent process of generating class estimates. Linguistically readable eOGS rules may assist physicians in explaining symptoms and making a diagnosis.
On Solvability Degree of Systems of Partial Fuzzy Relational Equations
Systems of partial fuzzy relational equations employing undefined values in the antecedents and consequents have been approached recently. The primary focus was on the issues of sufficient solvability…
Systems of partial fuzzy relational equations employing undefined values in the antecedents and consequents have been approached recently. The primary focus was on the issues of sufficient solvability and solvability criteria. This study introduces another perspective, investigating the behavior of solvability degrees of these systems. We employ operations from the Lower estimation and Dragonfly partial algebras developed in the partial fuzzy set theory framework. Initially, we establish a degree of solvability in an appropriate space of approximations containing potential solutions for the systems. Subsequently, we introduce the concept of the alpha-lift for a given partial fuzzy set and provide its fundamental properties. This concept is employed to modify the antecedents and consequents of a given system of partial fuzzy relational equations, resulting in a modified system. The solvability degree of this modified system is then studied, and we demonstrate that, under sufficient conditions, it significantly enhances the solvability degree of the initial system. This positive impact is observed in the Godel algebra, the underlying algebraic structure of partial algebras. In conclusion, we provide illustrative examples that effectively demonstrate the theoretical results.
Public Datasets from the Ecological Momentary Assessment Technical Pilot Study Conducted Among University Students (WP1.3, EMA1)
This repository contains data collected for Work Package 1.3: Short-term impacts of ICT use on adult wellbeing, part of the DigiWELL project (Research of Excellence on Digital Technologies and Wellbei…
This repository contains data collected for Work Package 1.3: Short-term impacts of ICT use on adult wellbeing, part of the DigiWELL project (Research of Excellence on Digital Technologies and Wellbeing, CZ.02.01.01/00/22_008/0004583). This study was conducted as a technical pilot (EMA1) to verify the functionality of the research applications used for subsequent EMA studies: Health React (for survey collection) and eBehave (for passive smartphone trace data collection), both developed by the University of Hradec Králové. The seven-day EMA study involved 58 Masaryk University students (58% female; age range: 19–30 years; M = 21.5; SD = 2.3). KeywordsEcological Momentary Assessment, Experience Sampling Method, Technical pilot For additional information or questions, please contact the contact person: Martin Tancoš (tancos@fss.muni.cz).
Trends in Sexual Initiation and Contraception Use Among Czech Adolescents between 2002-2022
Objectives: This study examined trends in sexual behaviour and the timing of sexual initiation among 15-year-old adolescents in Czechia between 2002 and 2022, with a focus on the age of sexual debut (…
Objectives: This study examined trends in sexual behaviour and the timing of sexual initiation among 15-year-old adolescents in Czechia between 2002 and 2022, with a focus on the age of sexual debut (i.e., 15 and older; early at 14; very early at 13 or younger). It also investigated trends in condom and hormonal contraceptive use at most recent intercourse. Methods: Data were drawn from six nationally representative waves of the Czech Health Behaviour in School-aged Children study, which was conducted between 2002 and 2022. Only 15-year-olds were included (N = 19,384). Descriptive trend analyses were conducted using survey weights, with subgroup comparisons by gender and age at sexual initiation. Results: The findings indicate a shift toward later sexual initiation, particularly among girls, with increasing proportions initiating at age 15 or older and declining initiation at age 14. A significant gender gap emerged in 2022, with fewer girls (13.9%) than boys (18.7%) reporting a sexual experience. The prevalence of very early initiation (age 13 or younger) remained stable over time, yet this group—especially boys—consistently accounted for a substantial minority of sexually active adolescents. Condom use declined from 81.2% to 69.9% across all initiation groups between 2014 and 2022, with the most persistent decline among very early initiators. Conclusions: The findings suggest a modest postponement of sexual debut among Czech adolescents and highlight a growing gender disparity in sexual activity by age 15. Persistent early initiation and declining condom use highlight the need for differentiated sexual health education strategies, particularly for younger initiators.
Positive body image is a pathway between nature contact and life satisfaction across 58 nations
When minorities clash: The role of intergroup contact, threat, and perceived discrimination in mutual attitudes of the Roma and Ukrainian Refugees
Empowerment or Pressure? Exploring the Impact of Female Body Depictions in Body Positivity Instagram Posts on Self-Objectification
Although Body Positivity content (BoPo) has been criticized for emphasizing physical appearance and promoting self-objectification, the specific features driving these effects remain unclear. The pres…
Although Body Positivity content (BoPo) has been criticized for emphasizing physical appearance and promoting self-objectification, the specific features driving these effects remain unclear. The present study examined whether depictions of female bodies act as triggers for self-objectification in BoPo on Instagram. In a between-subjects online experiment involving 158 women aged 18-29 (M = 21.6, SD = 2.4), exposure to female bodies in BoPo posts did not heighten state self-objectification. Trait self-objectification and negative mood did not moderate these effects; however, women with negative attitudes toward BoPo reported higher levels of state self-objectification. These findings underscore the potential importance of subjective appraisals in shaping the impacts of BoPo content. Overall, the study contributes to the ongoing debate about the potentially negative effects of BoPo on Instagram, suggesting that body depictions alone may not reinforce self-objectification. Future research should examine the distinct influence of different types of body portrayals to further clarify the impact of BoPo content on body image. From a practical perspective, prevention efforts and social media campaigns should aim to raise awareness of BoPo features that continue to overemphasize appearance, helping women better protect their body image from potential adverse effects.
Emotional Similarity Between Immigrants and Host Societies: The Link to Intergroup Relations and Well-Being
The network dynamics of antiprejudice norms: A fieldexperiment testing antiprejudice interventions inreal groups
This dataset contains information on students attending a psychology program. There are two tracks, one national and one international. The data also contains social network data, representing their r…
This dataset contains information on students attending a psychology program. There are two tracks, one national and one international. The data also contains social network data, representing their relationships. Students were subjected to a network experiment described in the article. The data belong to the following publications:Long, F., Scheepers, D., Zingora, T., & Pliskin, R. (2025). The network dynamics of antiprejudice norms: A field experiment testing antiprejudice interventions in real groups. Political Psychology, 00, 1–26. https://doi.org/10.1111/pops.70029
Physical Activity and Life Satisfaction Data from the 4HAIE Study for Modelling Nonlinear Dynamics
This repository contains the dataset and accompanying code used in the manuscript: "Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data." …
This repository contains the dataset and accompanying code used in the manuscript: "Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data." The dataset originates from the 4HAIE (Healthy Aging in Industrial Environment - Program 4) study, a 12-month intensive longitudinal study designed to examine within-person dynamics of health behaviors and well-being in everyday life (see corresponding protocol paper Elavsky et al., 2021). Dataset Contents The repository includes: Merged analysis dataset linking daily physical activity with corresponding end-of-day life satisfaction assessments. Daily physical activity data collected continuously over approximately 12 months using Fitbit Charge 3 and Fitbit Charge 4 devices. Physical activity is represented by daily step counts. Ecological Momentary Assessment (EMA) data collected during four 14-day measurement bursts distributed across the 12-month study period. Participants completed an end-of-day survey assessing life satisfaction using two items adapted from the Satisfaction with Life Scale (SWLS). Illustrative analysis code demonstrating the generalized additive modeling (GAM) approach used to investigate nonlinear relationships between physical activity and life satisfaction over time. Study Design Participants were monitored continuously for physical activity throughout the study while completing repeated intensive EMA assessments during four separate two-week measurement bursts. This design enabled examination of both long-term behavioral patterns and short-term fluctuations in subjective well-being within individuals. Variables The repository includes variables related to: Participant identifier (de-identified) Date of observation Daily step count (Fitbit) End-of-day life satisfaction ratings (two EMA items) Additional temporal variables required for longitudinal modeling Variable definitions and coding information are provided in the accompanying documentation. Purpose The dataset is intended to illustrate statistical approaches for analyzing intensive longitudinal health behavior data, with particular emphasis on nonlinear modeling of within-person associations using generalized additive models (GAMs). Rather than serving solely as an empirical example, the dataset demonstrates a methodological framework for capturing complex temporal relationships that may not be adequately represented using traditional linear approaches. Associated Publication This dataset accompanies the manuscript: Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data. If you use this dataset or the accompanying analysis code, please cite both this Zenodo repository and the associated publication. References: ELavsky, S., Brabec, M., Maly, M. (in review). Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data. Elavsky S, Jandačková V, Knapová L, Vašendová V, Sebera M, Kaštovská B, Blaschová D, Kühnová J, Cimler R, Vilímek D, Bosek T, Koenig J, Jandačka D. Physical activity in an air-polluted environment: behavioral, psychological and neuroimaging protocol for a prospective cohort study (Healthy Aging in Industrial Environment study - Program 4). BMC Public Health. 2021 Jan 12;21(1):126. doi: 10.1186/s12889-021-10166-4. PMID: 33435943; PMCID: PMC7801866. Elavsky S, Brabec M, Maly M, Knapova L, Kastovska B, Sebera M, Ely M, Jandackova VK, Keller J, Pavel M. The temporal dynamics of the association between daily physical activity and life satisfaction. Ann Behav Med. 2025 Jan 4;59(1):kaaf079. doi: 10.1093/abm/kaaf079. PMID: 41224246; PMCID: PMC12757008.
The new role of social work: the social worker-client relationship in the digitalised society as hotline-level bureaucracy
This article explores the evolving role of social workers in the context of increasing digitalisation, focussing on the Czech Republic. Using ecological systems theory and the shift from street-l…
This article explores the evolving role of social workers in the context of increasing digitalisation, focussing on the Czech Republic. Using ecological systems theory and the shift from street-level to screen-level bureaucracy as a framework, we analyse how digital tools are reshaping the relationship between clients and social workers, as well as professional boundaries. Using qualitative data from focus groups and interviews with social workers who support families at risk, we introduce the concept of ‘hotline-level bureaucracy’ to describe a recently emerged practice. In this model, social workers increasingly act as intermediaries between clients and digitalised institutions, often taking on responsibilities due to clients lacking access to technology and digital skills. This shift challenges the empowerment paradigm in social work, burdening practitioners with emotional and cognitive overload, and complicating ethical boundaries. We contend that this transformation necessitates a redefinition of roles, stronger institutional support, and broader structural responses to digital inequality.
Social robots in Czech residential care services: Comparing the perspectives of social work students and workers in homes for the older people
The aim of our research was to explore and compare the perspectives of students of social work and workers of homes for older people on the use of social robots in homes for older people. In our resea…
The aim of our research was to explore and compare the perspectives of students of social work and workers of homes for older people on the use of social robots in homes for older people. In our research, we used the standardized UNRAQ questionnaire (Tobis et al. 2021). We distributed a modified questionnaire to (1) students of social work (N = 140) who had taken a gerontology course. We also distributed the questionnaire among (2) workers of homes for older people (N = 133) who had not received any training in gerontotechnology to date. Subsequently, we organised a focus group with (14) students and interviews with (23) workers of homes for older people, including managers, social workers, direct-care workers, and health care workers. The quantitative data collected were evaluated using statistical procedures, and the qualitative data were subjected to thematic analysis. Students who were educated in gerontechnology accepted the use-potential of social robots in social work practice to a higher degree compared to workers who were not educated in this area. The research results showed that education in gerontechnology can play a key role in the adoption of social robots and their subsequent use in practice.
Well-being, digitisation, and social work: participatory strategies for inclusive digitisation in social services. The Catalan case
In this article, we analyse the digitalisation process in social services in Catalonia from the perspective of social workers' demands, establishing a set of strategies for better incorporation of dig…
In this article, we analyse the digitalisation process in social services in Catalonia from the perspective of social workers' demands, establishing a set of strategies for better incorporation of digital technologies in public administrations. Co-design and co-creation methodologies allow us to evaluate our organisations more effectively, giving social workers a voice so that they can highlight the positive and negative effects of their organisations' digitalisation model, actively participating in the redesign of the digital social services model from the outset. Through a participatory process involving 109 social workers from social services in Catalonia, using methodologies such as the customer journey and impact maps, this article presents some strategies for strengthening inclusive digitalisation focused on the well-being of social service workers and users.
From competence to care: digital leadership in eHealth
This article examines the role of digital skills in the health sector, where healthcare social workers perform their professional work, using the Delphi method in two rounds: 2020 (pre-Covid-19 contex…
This article examines the role of digital skills in the health sector, where healthcare social workers perform their professional work, using the Delphi method in two rounds: 2020 (pre-Covid-19 context) and 2021 (Covid-19 context). Experts point out three major transformations in organisations resulting from digitalisation: (i) the growing importance of digital skills; (ii) the relevance of developing a leadership adapted to a digitalisated context, and (iii) the key role of health managers and their organisations. Based on the results obtained, a conceptual approach based on the Shaw et al. (2017) model is proposed to face the challenges of e-Health, explicitly centred on fostering both organisational and individual wellbeing. This leadership model aims to improve the wellbeing of workers in the healthcare sector, including healthcare social workers.
DXAnalyzer
DXAlyzer is a desktop tool for extracting, reviewing, validating, and exporting DXA measurement data from Hologic DXA PDF reports and OCR text exports. The application runs locally, stores data in a l…
DXAlyzer is a desktop tool for extracting, reviewing, validating, and exporting DXA measurement data from Hologic DXA PDF reports and OCR text exports. The application runs locally, stores data in a local SQLite database, and prepares cohort-level CSV tables and SVG figures for research and publication workflows. Features Import DXA reports from .pdf, .txt, .text, and .ocr files. Extract patient identifiers, scan dates, BMD, BMC, T-score, Z-score, fat mass, lean mass, and total mass values. Review and edit extracted records before saving them. Validate measurements with QC status and notes. Compare repeated measurements for the same patient. Export full measurement data to CSV. Generate publication summary tables and subject-level change tables. Export cohort figures as SVG. Manage parser templates for different report formats. Requirements For running from source: Python 3.11 or newer Windows, macOS, or Linux with Tkinter support Python dependencies are listed in requirements.txt. Run From Source python -m pip install -r requirements.txt python main.py Application data is stored locally in: ~/DXAlyzer/dxa.db ~/DXAlyzer/templates/ Windows Executable The Windows executable is built with PyInstaller and published through GitHub Actions. To download it: Open the repository on GitHub. Go to Releases. Download DXAlyzer.exe from the latest release. If there is no release yet, open Actions, run the Windows Release workflow manually, and download the windows-DXAlyzer artifact. Build On Windows From a Windows command prompt in the project folder: build.bat Expected output: dist\DXAlyzer.exe More release and code-signing details are documented in WINDOWS_RELEASE.md. Basic Workflow Import PDF reports or OCR text exports. Review extracted records before saving. Check measurement quality in the QC tab. Run cohort analysis. Export CSV tables and SVG figures. Acknowledgements This software was developed as part of the project Research of Excellence on Digital Technologies and Wellbeing, CZ.02.01.01/00/22_008/0004583, co-funded by the European Union. License This project is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Reference and Solution Architecture for GenAI- and GIS-Enhanced Physical Activity Interventions: Towards Implementing the AI4Motion Platform
Abstract Digital Behaviour Change Interventions (DBCIs) aim at improving individual health by engaging various means of Information and Communication Technology (ICT), including mobile apps and weara…
Abstract Digital Behaviour Change Interventions (DBCIs) aim at improving individual health by engaging various means of Information and Communication Technology (ICT), including mobile apps and wearables. Participant intervention fatigue may happen when DBCIs become too frequent, repetitive, demanding, or lack perceived relevance, and this may result in participants’ reduced motivation and adherence over time. Advancing technology-supported engagement mechanisms is therefore of utmost importance. To address this problem, we present a reference and solution architecture based on open-source technologies and open Application Programming Interfaces (Open APIs). First, we integrated a Large Language Model (LLM) component into the DBCI design. Second, to support context-awareness, we enhanced this integration by adding a Geographic Information Systems (GIS) element. Our pilot implemented AI4Motion platform targets both personalization and contextualization aspects of DBCIs. Our work contributes to the emerging discussion on LLM/GIS-related system design patterns for digital platforms supporting Ecological Momentary Assessment (EMA), Experience Sampling Method (ESM), and Just-in-Time Adaptive Interventions (JITAIs).
Leveraging Generative Artificial Intelligence to Enhance Carbon Performance in Supply Chains Through Green Product Innovation and End-of-Life Product Management: AI-Driven Carbon Performance
ABSTRACT This study illustrates how organizations reconcile their information processing capabilities with uncertainty within the supply chain (SC) through generative artificial intelligence (GAI) to…
ABSTRACT This study illustrates how organizations reconcile their information processing capabilities with uncertainty within the supply chain (SC) through generative artificial intelligence (GAI) to achieve carbon performance (CP). A quantitative research methodology is applied, and 155 responses from manufacturing firms are analyzed through structural equation modeling (SEM) for hypothesis testing. The findings suggest that GAI for process automation and cognitive engagement has a positive influence on business intelligence (BI), whereas end-of-life (EOL) product management mediates the relationship between green product innovation (GPI) and CP. This study contributes to the SC context, focusing on GAI and BI in mitigating uncertainties within SCs to foster GPI and improve CP. This study highlights actionable frameworks for leveraging digital technologies in sustainable SCs by addressing technological challenges and integrating green innovation practices
Analysis of Participant-Level Characteristics Predicting Adherence to Long-Term EMA and Fitbit Monitoring
This directory contains R scripts, data files, and analysis reports relatedto the analysis of adherence. FILE OVERVIEW DATA FILES* data.xlsx Main subject-level dataset. * data-fitbit.xlsx …
This directory contains R scripts, data files, and analysis reports relatedto the analysis of adherence. FILE OVERVIEW DATA FILES* data.xlsx Main subject-level dataset. * data-fitbit.xlsx Dataset with detailed Fitbit-derived measures. DATA LOADING AND PREPROCESSING* data.R R script for loading and preprocessing the main dataset. * data-fitbit.R R script for loading and preprocessing the detailed Fitbit dataset. DESCRIPTIVE AND BASELINE ANALYSES* summary.Rmd R Markdown document providing a basic description of the datasets. * baseline.Rmd R Markdown document with baseline characteristics and missing-value analysis, including evaluation and imputation of missing values in SWL (new variable SWLlm.predicted). * baseline-by-burst.Rmd R Markdown document with baseline characteristics stratified by burst number. MODELING AND VARIABLE IMPORTANCE* importance.Rmd R Markdown analysis template for evaluating variable importance using stepwise regression and random forest models. PROJECT CONFIGURATION AND BUILD SYSTEM* Makefile.R Project definition for the rmake package; generates the GNU Makefile. * Makefile File dependencies and compilation commands used by GNU Make to generate all project results. * Rproject.Rproj RStudio project file. ------------------------------------------------------------------------------REQUIREMENTS------------------------------------------------------------------------------ 1) INSTALL REQUIRED R PACKAGES Run the following commands in R: install.packages(c( "tidyverse", "knitr", "rmake", "caret", "randomForest", "fastDummies", "broom", "devtools" )) devtools::install_github("beerda/hammer") devtools::install_github("beerda/mbrtools") 2) GENERATE PROJECT ANALYSES Run the analysis pipeline using rmake: make() This command generates all results defined in the project Makefile.------------------------------------------------------------------------------
Criminalisation of truancy as a manifestation of advanced marginality that mothers should be blamed for: Media framing in the Czech Republic
In this study, we present an analysis, driven by the Critical Discourse Studies, Frame Analysis, and Narrative Analysis, of how truancy is represented in the Czech media. Based on our findings, we ass…
In this study, we present an analysis, driven by the Critical Discourse Studies, Frame Analysis, and Narrative Analysis, of how truancy is represented in the Czech media. Based on our findings, we assert that Czech media employ three frames (gender bias; moralisation and individualisation; and repression/retribution) for representing truancy, effectively depicting it as a criminal problem created by irresponsible mothers (and children they neglected) who must be punished to address the issue. Employing the cultural and feminist criminology framework, as a combination that is scarcely used in studies, we argue that media (re)produce criminalisation of mothers by amplifying deeply rooted and routinised gendered cultural stereotypes. In this sense, we show that cultural criminology is useful not only for analysing adrenaline/“exciting” transgression, subcultures, or edgework, but, especially when combined with feminist criminology, also for analysing possibilities of criminalisation rooted in mainstream culture. Furthermore, our analysis shows that Czech media representation of truancy is damaging both for society and for addressing the issue, reducing it to a mere neoliberal individual choice of irresponsible mothers, even though truancy is a much more complex phenomenon with communal and structural levels and can be seen rather as a product of advanced marginality.
Usability and Feasibility of a Contrast Avoidance Model-Based Virtual Reality Protocol Designed for Generalized Anxiety Disorder
Generalized anxiety disorder (GAD) is characterized by persistent, excessive, and difficult-to-control worry. The Contrast Avoidance Model (CAM) proposes that individuals with GAD use worry to sustain…
Generalized anxiety disorder (GAD) is characterized by persistent, excessive, and difficult-to-control worry. The Contrast Avoidance Model (CAM) proposes that individuals with GAD use worry to sustain negative emotional arousal, thereby avoiding sharp negative emotional contrasts that would otherwise follow unexpected adverse events. A virtual reality (VR) protocol was developed to simulate such contrasts by alternating guided relaxation with brief anxiety-inducing scenarios (skyline plank, crowded elevator, and loose dog encounter). This study evaluated the usability and feasibility of this protocol in 20 subclinical adults aged 18–45 who met a screening threshold of GAD-7 ≥ 5, using a Meta Quest 3 headset and Polar H10 heart rate sensor. Exposure segments produced a significant decrease in RMSSD (β = −0.185, p < 0.001), consistent with reduced parasympathetic activity during exposure, whereas heart rate did not differ significantly between conditions. Subjectively, exposure increased SUDS (β = 2.23, p < 0.001) and SAM arousal (β = 1.95, p < 0.001), and decreased SAM valence (β = −2.68, p < 0.001) and dominance (β = −1.70, p = 0.005). Presence scores, cybersickness ratings, and qualitative feedback supported the usability of the protocol and identified concrete design refinements. These results support the feasibility of the protocol and provide a foundation for future controlled clinical evaluation.
Data for "Trends in adolescent cigarette smoking in Czechia: findings from the HBSC study 2014–2022"
This dataset contains aggregated prevalence estimates regarding cigarette smoking among adolescents in the Czech Republic, derived from three waves of the Health Behaviour in School-aged Children (HBS…
This dataset contains aggregated prevalence estimates regarding cigarette smoking among adolescents in the Czech Republic, derived from three waves of the Health Behaviour in School-aged Children (HBSC) study conducted in 2014, 2018, and 2022. The data represents a total sample of 29,525 respondents (14,761 boys and 14,764 girls) across three target age groups: 11, 13, and 15 years old.
Trends in adolescent cigarette smoking in Czechia: findings from the HBSC study 2014–2022
Objectives: Regular monitoring of health-related behaviours among vulnerable populations is of public health importance. This study examines recent trends in adolescent cigarette smoking in Czech…
Objectives: Regular monitoring of health-related behaviours among vulnerable populations is of public health importance. This study examines recent trends in adolescent cigarette smoking in Czechia following the marked changes reported in the mid-2010s.Methods: Data from three recent rounds of the Health Behaviour in School-aged Children (HBSC) study conducted in Czechia in 2014, 2018 and 2022 were analysed. Temporal trends were assessed for two indicators of adolescent cigarette use: lifetime cigarette use and cigarette use in the last 30 days. Survey-adjusted binary logistic regression models were used to test changes between survey periods. In 2022, the prevalence of electronic cigarette use was additionally estimated using the same indicators.Results: A continued decline in adolescent cigarette use was observed for both indicators, extending the downward trends reported in the mid-2010s into the 2020s. The decline was most pronounced between 2014 and 2018, with smaller but persistent decreases thereafter, particularly among older adolescents. However, the findings also highlight the substantial prevalence of electronic cigarette use. In 2022, more than one-third of 15-year-olds in Czechia reported lifetime electronic cigarette use (35.1% among boys and 36.6% among girls), and approximately one in five reported use in the last 30 days (19.6% among boys and 23.0% among girls).Conclusions: While conventional cigarette use among adolescents continues to decline, electronic cigarette use represents an important component of contemporary adolescent smoking-related behaviour. In the long term, the phenomenon of electronic cigarettes may counteract intended trends in nicotine-related harms. These findings underscore the need for continued surveillance and prevention efforts in Czechia that address both conventional and emerging smoking-related products.
Mental health stigma and its consequences: a systematic scoping review of pathways to discrimination and adverse outcomes
Current research evaluating the consequences of stigma towards people with mental illness is not nuanced in emphasizing the critical distinction between stigma as negative attitudes and discrimination…
Current research evaluating the consequences of stigma towards people with mental illness is not nuanced in emphasizing the critical distinction between stigma as negative attitudes and discrimination as harmful behaviours that limit access to services, employment, and social inclusion. Understanding these distinctions is essential for designing targeted, evidence-based universal, targeted and indicated interventions to improve the quality of life and well-being. This review evaluates the evidence on the consequences of stigma towards people with mental illness. Using PRISMA guidelines, we analysed 448 studies (294 quantitative, 154 qualitative) investigating stigma's negative outcomes. Findings were categorized into health, service use, psychosocial, economic, and structural impacts. Although stigma is consistently associated with adverse outcomes across life domains, evidence of a causal link between negative attitudes and poorer outcomes for individuals with mental disorders remains limited. Furthermore, there is a striking scarcity of research from low- and middle-income countries, with significant regional gaps, and studies addressing structural stigma embedded in societal institutions are particularly rare. Efforts to combat stigma must distinguish between attitudes and behaviours, focusing on reducing discrimination while enhancing public mental health literacy and access to effective interventions. Tackling these challenges requires a comprehensive, evidence-informed approach to improving mental health outcomes for all.
Mental health in Central and Eastern Europe: a comprehensive analysis
The post-communist WHO European region, often called Central and Eastern Europe (CEE), includes 28 countries with over 770 million people. Mental health systems remain shaped by the communist legacy o…
The post-communist WHO European region, often called Central and Eastern Europe (CEE), includes 28 countries with over 770 million people. Mental health systems remain shaped by the communist legacy of centralized institutions, a narrow biomedical focus, and neglect of social and psychological dimensions. Chronic underfunding persists, further strained by shrinking civic space in some countries and the war in Ukraine. Substantial progress has been made in the past decade, with modernization and rights-based approaches gaining ground. Yet reforms face entrenched barriers: underinvestment disproportionate to the burden; pervasive stigma, weak advocacy, and limited involvement of people with lived experience; dominance of institutional care over prevention, promotion, and community services; reliance on donor-driven projects that falter once funding ends; and human resource problems. Governance is often unstable, with low prioritization, clientelism, and personal biases undermining reforms. Research and data remain scarce, leaving systems unevaluated and vulnerable to reversal. Poor decision-making compounds these barriers: systemic missteps, driven by limited expertise, weak evidence, and personal biases, prevent resources from achieving the best possible outcomes. To move forward, CEE must integrate health, social, and education systems, secure sustainable crisis services, strengthen professional skills, involve people with lived experience, expand public mental health expertise, and, above all, commit greater and more transparent investment, closer to western European levels, if resilient and effective systems are to be built.
Generalizovaná úzkostná porucha a její léčba
Generalizovaná úzkostná porucha (GAD) se vyznačuje chronickými a nadměrnými obavami v různých aspektech každodenního života, včetně osobních a p…
Generalizovaná úzkostná porucha (GAD) se vyznačuje chronickými a nadměrnými obavami v různých aspektech každodenního života, včetně osobních a pracovních povinností, zdraví či mezilidských vztahů. Diagnostická kritéria procházejí mírnou úpravou při přechodu Mezinárodní klasifikace nemocí z verze 10 (MKN-10) na novou verzi, MKN-11. V našem článku se podíváme na změny v nové klasifikaci nemocí, která by se měla stát diagnostickým vodítkem v nadcházejících letech. Cílem tohoto článku je podrobněji prozkoumat charakteristiky generalizované úzkostné poruchy, její diagnostické výzvy a aktuální postupy v léčbě, včetně nejmodernějších přístupů. Léčba GAD obvykle zahrnuje farmakoterapii, psychoterapii nebo jejich kombinaci. Častými komplikacemi úspěšné léčby jsou vedlejší účinky léků nebo nedostatečná odpověď na léčbu až rezistence. Nadějí pro pacienty může být využití moderních technologií v léčbě generalizované úzkostné poruchy, jakými je například virtuální realita, kterou využíváme v našem centru Výzkumu virtuální reality v duševním zdraví a neurovědách v Národním ústavu duševního zdraví při léčbě úzkostných poruch, a to včetně GAD. Generalized Anxiety Disorder (GAD) is characterized by chronic and excessive worries across various aspects of daily life, including personal and work-related responsibilities, health, and interpersonal relationships. The diagnostic criteria undergo a slight modification with the transition from the 10th edition of the International classification of diseases (ICD-10) to the new version, ICD-11. This article examines the changes in the new classification, which is expected to become the diagnostic guideline in the coming years. The aim of this article is to explore in detail the characteristics of generalized anxiety disorder, its diagnostic challenges, and current treatment approaches, including the most modern techniques. GAD treatment typically involves pharmacotherapy, psychotherapy, or a combination of both. Common complications in the successful treatment of GAD include side effects of medications or insufficient response to treatment, including resistance. A promising approach for patients may be the use of modern technologies in the treatment of generalized anxiety disorder, such as virtual reality used at our Virtual reality research center for mental health and neurosciences at the National institute of mental health in the treatment of anxiety disorders, including GAD.
Supplementary data for "Changes in stigma and population mental health literacy before and after the Covid-19 pandemic: Analyses of repeated cross-sectional studies"
The data come from cross-sectional surveys conducted on representative samples of the non-institutionalised adult population in the Czech Republic in 2017, 2019 and 2022. The data include basic demogr…
The data come from cross-sectional surveys conducted on representative samples of the non-institutionalised adult population in the Czech Republic in 2017, 2019 and 2022. The data include basic demographic data and data from four questionnaires. Data on mental health problems were assessed using the Mini International Neuropsychiatric Interview (M.I.N.I.) in 2017 and 2022. The Self-identification of Mental Illness (SELF-I) scale was used to assess self-identification as having a mental illness in these years. Stigma associated with mental health was assessed using the Reported and Intended Behaviour Scale (RIBS) and the Community Attitudes towards Mental Illness (CAMI) scale in 2019 and 2022. Data pocházejí z průřezových šetření provedených na reprezentativních vzorcích neinstitucionalizované dospělé populace v České republice v letech 2017, 2019 a 2022. Data zahrnují základní demografické údaje a údaje ze čtyř dotazníků. Údaje o problémech v oblasti duševního zdraví byly hodnoceny pomocí Mini mezinárodního neuropsychiatrického rozhovoru (M.I.N.I.) v letech 2017 a 2022. K posouzení sebeidentifikace jako osoby s duševním onemocněním byla v těchto letech použita škála Self-identification of Mental Illness (SELF-I). Stigmatizace spojená s duševním zdravím byla hodnocena pomocí škály Reported and Intended Behaviour Scale (RIBS) a škály Community Attitudes towards Mental Illness (CAMI) v letech 2019 a 2022.
Excess mortality in people hospitalised for alcohol use disorders before and during the pandemic – A registry-based retrospective cohort study
Introduction The aim was to analyse mortality and estimate the life expectancy among people hospitalised for alcohol use disorders (AUD) compared with the general Czech population aged ≥20 y…
Introduction The aim was to analyse mortality and estimate the life expectancy among people hospitalised for alcohol use disorders (AUD) compared with the general Czech population aged ≥20 years. A temporal perspective on excess mortality was used, covering three recent calendar periods before and during the pandemic. Methods Three retrospective cohorts of the target population were constructed using registry-based data. The target population was defined as all adult patients (aged ≥20 years) admitted to the hospital for AUD (ICD-10 dg. of F10.x) between 2010 and 2021. Age-adjusted mortality rates and life expectancies were calculated for the comparative analysis. Official Czech mortality and vital statistics were used for the comparison. A Poisson log-linear regression model was used to test the effect of the pandemic period (2020–2021) on mortality in the AUD target population. Results At age 20, the estimated life expectancy of the AUD target was 21–27 years less than that of the Czech general population. Excess mortality was relatively highest in young people aged 20–34 years and in adults aged 35–49 years. During the pandemic period 2020–2021, mortality rates in the target AUD increased significantly. However, relative inequalities with the general Czech population did not change significantly. Discussion and Conclusions People hospitalised for AUD have much higher mortality rates, resulting in markedly reduced life expectancy. During the pandemic, their mortality rates increased even more. However, the increase was no greater than in the general Czech population.
Changes in stigma and population mental health literacy before and after the Covid-19 pandemic: analyses of repeated cross-sectional studies
The Covid-19 pandemic and related social restrictions have been associated with increased rates of mental health problems, prompting a global surge in interest in mental well-being, which might …
The Covid-19 pandemic and related social restrictions have been associated with increased rates of mental health problems, prompting a global surge in interest in mental well-being, which might have had a positive effect on population mental health literacy (MHL). We aimed to compare levels of mental health related stigma among the Czech general adult population before and after the Covid-19 pandemic, as well as recognition of own mental health problems, among those members of the general population who screened positively for mental disorders. Methods: We conducted a comprehensive analysis of multiple almost identically designed cross-sectional surveys carried out on representative samples of the non-institutionalized adult population in Czechia in 2017, 2019, and 2022. Mental health problems were assessed using the Mini International Neuropsychiatric Interview (M.I.N.I.) in 2017 and 2022, while Self-identification of Mental Illness Scale (SELF-I) gauged self-recognition in 2017 and 2022. Mental health-related stigma was evaluated using the Reported and Intended Behaviour Scale (RIBS) and the Community Attitudes towards Mental Illness scale (CAMI) in 2019 and 2022. Results: Attitudes towards individuals with mental health problems exhibited no statistically significant change; however, reported and intended behaviours, i.e. proxies of social distance, changed for the better. Also, self-recognition of mental health problems demonstrated statistically significant improvements among those screening positive for depression, anxiety, and suicide risk, but not among alcohol use disorders. Conclusions: Population MHL remains low and recent positive changes are likely more attributable to the Covid-19 pandemic and related increase in interest in mental health than to deliberate efforts by government or state or other entities. This underscores the complex interplay between societal factors and mental health outcomes, warranting further exploration and reconsideration of public mental health strategies.
The Journey From Nonimmersive to Immersive Multiuser Applications in Mental Health Care: Systematic Review
Over the past 25 years, the development of multi-user applications has seen significant advancements and challenges. The technological development in this field has emerged from simple chatrooms, thro…
Over the past 25 years, the development of multi-user applications has seen significant advancements and challenges. The technological development in this field has emerged from simple chatrooms, through videoconferencing tools to the crea-tion of complex, interactive, and often multisensory virtual worlds. These multi-user technologies have gradually found their way into mental health care, where they are used in both dyadic counselling and group interventions. However, some limitations in hardware capabilities, user experience designs, and scalability may have hindered the effectiveness of these applications. Objective: The present systematic review aimed at summarizing the progress made and the potential future directions in this field while evaluating various factors and perspectives relevant to remote multi-user interventions. Methods: The systematic review was performed based on Web of Science (WoS) and PubMed database search covering articles in the English language published from Jan-uary 1999 to March 2024 related to multi-user mental health interventions. Several inclusion and exclusion criteria were determined before and during the records screening process performed in several steps. Results: We have identified 49 records exploring the multi-user applications in mental health care, ranging from text-based interventions to interventions set in fully immer-sive environments. The number of publications exploring this topic is growing since 2015, with a large increase during COVID-19 pandemic. The majority of digital inter-ventions were delivered in a form of video-conferencing, with only a few implementing immersive environments. The studies utilized professional or peer supported group interventions or a combination of both approaches. The research studies targeted di-verse groups and topics, from nursing mothers to psychiatric disorders or various mi-nority groups. Most group sessions happened weekly, or in case of the peer-support groups, often with flexible schedule. Conclusions: We have identified many benefits to multi-user digital interventions for mental healthcare. These approaches provide distributed, always available and afford-able peer support that can be used to deliver necessary help to people living outside of areas where in-person interventions are easily available. While immersive virtual envi-ronments have become a common tool in many areas of psychiatric care, such as expo-sure therapy, our results suggest that this technology in multi-user settings is still in its early stages. Most identified studies investigated mainstream technologies, such as vid-eo conferencing or text-based support, substituting immersive experience for conven-ience and ease of use. While many studies discuss useful features of virtual environ-ments in group interventions, such as anonymity or stronger engagement with the group, we discuss persisting issues with these technologies, which currently prevent their full adoption.
Data for "Trends in alcohol use among Czech adolescents: findings from the HBSC study 2014–2022"
This dataset contains aggregated prevalence estimates regarding alcohol consumption among adolescents in the Czech Republic, based on three waves of the Health Behaviour in School-aged Children (HBSC)…
This dataset contains aggregated prevalence estimates regarding alcohol consumption among adolescents in the Czech Republic, based on three waves of the Health Behaviour in School-aged Children (HBSC) study conducted in 2014, 2018, and 2022. The data represents a total sample of 29,525 respondents (14,761 boys and 13,764 girls) across three target age groups: 11, 13, and 15 years old.
Sensory processing sensitivity and its associations with guilt, shame, self-esteem, and neuroticism
Abstract Background Sensory Processing Sensitivity (SPS) is a trait linked to deeper processing of stimuli and heightened emotional reactivity. These characteristics suggest a potential link to more …
Abstract Background Sensory Processing Sensitivity (SPS) is a trait linked to deeper processing of stimuli and heightened emotional reactivity. These characteristics suggest a potential link to more intense self-conscious emotions. The objective of this study was to investigate the associations between SPS, feelings of guilt, shame, and self-esteem, and to test whether these relationships are independent of the influence of neuroticism. Methods We conducted a cross-sectional study using data from an online survey of Czech adults (n = 1012; 49.3 ± 16.7 years, 50.4% female). Participants completed measures of SPS (Sensory Processing Sensitivity Questionnaire, SPSQ; Highly Sensitive Person Scale), feelings of guilt and shame (Guilt and Shame Experience Scale), self-esteem (Rosenberg Self-Esteem Scale), and neuroticism (the neuroticism subscale of the Big Five Inventory). The associations were examined using linear and logistic regression models, with adjustments for neuroticism and key demographic variables. Results Linear regression analyses showed that higher SPS was significantly associated with increased feelings of guilt and shame, and with lower self-esteem. After adjustment for neuroticism, the association between SPS and self-esteem was no longer significant (β ≈ 0.03, p > 0.05), whereas the β-coefficients for feelings of guilt and shame were reduced but remained significant. Logistic regression analyses comparing low, medium, and high SPS groups and, separately, the equivalent levels on the Sensory Sensitivity subscale of the SPSQ, indicated that highly sensitive individuals were more likely to report feelings of guilt alone and combined guilt and shame, with odds ratios (ORs) ranging from 4.42 (95% CI 2.17–8.99, p < 0.001) to 6.38 (95% CI 3.08–13.25, p < 0.001). No significant associations emerged between SPS and feelings of shame alone or low self-esteem. Analyses using alternative SPS measures yielded broadly similar results, with the Highly Sensitive Person Scale showing even stronger associations with feelings of guilt and shame, while again no effect was found for self-esteem. Conclusions Highly sensitive individuals appear to be more prone to experiencing heightened feelings of guilt and, to a lesser degree, shame. However, the initially observed negative association between SPS and self-esteem was no longer significant after neuroticism was included in the model.
Trends in alcohol use among Czech adolescents: findings from the HBSC study 2014–2022
Objectives: The present study aims to examine trends in adolescent alcohol use over the period from 2014 to 2022.Methods: Data from the last three Health Behaviour in School-aged Children (H…
Objectives: The present study aims to examine trends in adolescent alcohol use over the period from 2014 to 2022.Methods: Data from the last three Health Behaviour in School-aged Children (HBSC) surveys conducted in 2014, 2018 and 2022 were used for this study. Three measures of adolescent alcohol use have been chosen for analyses: lifetime alcohol use, last 30 days alcohol use, and repeated lifetime drunkenness. The analyses comprised calculation of period-specific prevalence estimates and testing of the significance of between-period changes using survey-adjusted logistic regression models.Results: Comparing prevalence rates between the periods, consistent decrease in adolescent alcohol use becomes apparent, particularly for drop of rates in 2018 compared to those in 2014. The corresponding data on the prevalence of lifetime alcohol use among 13-year-old boys was 59.7% in 2014 and 44.2% in 2018; and among 15-year-old boys 80.4% in 2014 and 74.9% in 2018. For 13-year-old girls, the estimated prevalence was 46.9% in 2014 and 41.1% in 2018; and for 15-year-old girls 83.7% in 2014 and 75.9% in 2018. This is the case for repeated lifetime drunkenness, and the decrease is consistent across boys and girls, as well as the respective age groups. In survey waves 2018 and 2022, we do not see a statistically significant decline, but rather a stabilisation of assessed prevalence at a level from the previous wave of the study.Conclusions: The decline in alcohol use among Czech adolescents is part of a global trend of reducing alcohol drinking among young people, on the background of social mechanisms including the change of cultural status of alcohol and changes in young people's leisure preferences.
Životní styl a pohybové chování rodin s 3-8letými dětmi
The long-term research focus on the analysis of the lifestyle of families with prepubertal children and the dismal state of the increase in overweight and obesity in Czech adolescents led the authors …
The long-term research focus on the analysis of the lifestyle of families with prepubertal children and the dismal state of the increase in overweight and obesity in Czech adolescents led the authors to conduct research with families with children aged 3-8 years with the intention of uncovering patterns of all-day movement behaviour, including sleep and physical activity (PA), and formulating understandable recommendations aimed a tanchoring their healthy lifestyle. The presented monograph is methodologically based on a validated instrumental 24-hour monitoring using an accelerometer placed on the wrist of the non-dominant hand capable of detecting even the slightest human movement. A total of 396 families with at least one child aged 3-8 years completed this multiday monitoring of movement behavior.
TRENDS IN THE PERCEPTION OF SCHOOL CLIMATE: HBSC STUDY IN THE CZECH REPUBLIC 1994–2022
Objective: This study aimed to examine long-term trends in Czech adolescents’ perceptions of school climate between 1994 and 2022, focusing on school satisfaction, perceived school pressure, and…
Objective: This study aimed to examine long-term trends in Czech adolescents’ perceptions of school climate between 1994 and 2022, focusing on school satisfaction, perceived school pressure, and perceived social support from classmates and teachers. Methods: Data were drawn from eight cycles of the Czech Health Behaviour in School-aged Children (HBSC) study, encompassing responses from 63,252 students aged 11, 13, and 15. Binary logistic regression analyses were conducted to assess temporal trends and associations between school climate indicators and demographic variables, including gender, age, and family affluence. Results: Findings revealed a pronounced decline in school satisfaction and a significant increase in perceived school pressure, with 2022 showing the lowest satisfaction and highest pressure levels. Perceived support from classmates and teachers declined, especially among students in older age groups and among girls. Socioeconomic disparities had a modest but consistent impact, with students from more affluent backgrounds reporting more favorable school experiences. Conclusions: Czech adolescents’ school climate perceptions have deteriorated over the past three decades, marked by rising demands and weakening support at school. These trends may contribute to reduced school engagement and heightened psychological distress, emphasizing the need for systemic interventions that ensure that high school expectations are coupled with relational and emotional support in schools.
Trends in alcohol use among Czech adolescents, 2014–2022: Findings from the HBSC study
Objectives The present study aims to examining trends in adolescent alcohol use over the period from 2014 to 2022. Methods Data from the last three HBSC surveys conducted in 2014, 2018, and 2022 were …
Objectives The present study aims to examining trends in adolescent alcohol use over the period from 2014 to 2022. Methods Data from the last three HBSC surveys conducted in 2014, 2018, and 2022 were used for this study. Three measures of adolescent alcohol use have been chosen for analyses: lifetime alcohol use, last 30 days alcohol use, and repeated lifetime drunkenness. The analyses comprised calculation of period specific prevalence estimates and testing of the significance of between-period changes using survey-adjusted logistic regression models. Results Comparing prevalence rates between the periods, consistent decrease in adolescent alcohol use becomes apparent, particularly for drop of rates in 2018 compared to those in 2014. This is the case for lifetime alcohol use and repeated lifetime drunkenness, and is consistent across boys and girls, as well as the respective age groups. In survey waves 2018 and 2022, we do not see a statistically significant decline, but rather a stabilisation of assessed prevalence at a level from the previous wave of the study. Conclusions The decline in alcohol use among Czech adolescents is part of a global trend of reducing alcohol drinking among young people, on the background of social mechanisms including the change of cultural status of alcohol and changes in young people's leisure preferences.
Trends in sleep patterns among Czech adolescents and their current correlates of late bedtimes and social jet lag: HBSC study 2014-2022
Objectives: Sleep is vital for maintaining the health and wellbeing of people of all ages. However, for adolescents, sufficient sleep of adequate duration and quality is critical for profound mental, …
Objectives: Sleep is vital for maintaining the health and wellbeing of people of all ages. However, for adolescents, sufficient sleep of adequate duration and quality is critical for profound mental, physical, social, and emotional development. This study aimed to describe trends in sleep duration and late bedtime during school and non-school days in representative cohorts of 11-, 13-, and 15-year-old adolescents from Czechia from 2014 to 2022, and to examine the current associations between late bedtimes/social jet lag and wellbeing indicators among adolescents in 2022. Methods: The analysed sample of 42,101 adolescents aged 10.5-16.5 years was drawn from three nationally representative cohorts of Czech schoolchildren from the last three cycles of the Health Behaviour in School-aged Children study, conducted between 2014 and 2022. Results: Mean sleep duration (hours:minutes) on school and non-school days significantly (p < 0.05) decreased for both boys (schooldays: 8:192014→7:592022; non-school days: 9:362018→9:232022) and girls (schooldays: 8:202014→7:552022; non-school days: 9:582018→9:412022) between 2014/2018 and 2022, while the prevalence of insufficient sleep significantly (p < 0.001) increased over the same period (boys schooldays: 35.4%2014→49.2%2022, boys non-school days: 14.9%2018→18.0%2022; girls schooldays: 35.1%2014→51.7%2022, girls non-school days: 9.8%2018→13.3%2022). Adolescents with late bedtimes or social jet lag (> 2 hours) had significantly higher odds (p < 0.001) of skipping breakfast daily, drinking energy drinks daily, being drunk at least twice in their lifetime, experiencing reduced psychological wellbeing and low life satisfaction, reporting irritability, and problematic social media use and internet gaming than those with earlier bedtimes or without social jet lag. Conclusions: It is highly desirable that families, in close cooperation with schools and professional representatives, make efforts to ensure adherence to the recommended length and quality of sleep, as the trend results indicate worsening sleep patterns, deepening social jet lag, and a disturbing increase in adolescent risk behaviours and health complaints related to insufficient sleep.
CHANGES IN SOCIAL MEDIA USE PATTERNS AMONG CZECH ADOLESCENTS: HBSC STUDY 2018–2022
Objectives: Previous studies have identified four distinct patterns of adolescent social media use (SMU): (1) Non-active users abstain from social media or engage in online interactions only once a we…
Objectives: Previous studies have identified four distinct patterns of adolescent social media use (SMU): (1) Non-active users abstain from social media or engage in online interactions only once a week or less; (2) Active users connect with others online daily without any functional impairments related to their SMU; (3) Intense users frequently engage with others online but do not meet criteria for problematic use; (4) Problematic users report six or more addiction-like symptoms. The following study aimed to assess the prevalence of these SMU patterns among Czech adolescents; examine changes between 2018 (pre-COVID-19) and 2022; and explore age and gender differences to identify at-risk subgroups. Methods: Data were drawn from the Health Behaviour in School-aged Children (HBSC) study among 11-, 13-, and 15-year-olds. The study analysed Czech data from the 2017/18 and 2021/22 waves (n = 26,450). Results: Findings revealed marked changes in SMU patterns between 2018 and 2022 among Czech adolescents. Girls and older adolescents reported higher rates of problematic SMU, which increased steadily with age. The share of non-active users declined, most notably among 11-year-olds. Conclusions: The marked increase in both intense and problematic SMU among Czech adolescents highlights a growing public health concern. Given the established associations between problematic SMU and poorer mental health outcomes, these findings call for the integration of digital behaviour monitoring and education into school-based mental health and prevention programs. Particular attention should be given to early adolescence and to gender-specific vulnerabilities.
Overweight, Obesity, and Body Weight Perception among Czech Adolescents: A Two-Decade Analysis (HBSC Study 2002-2022)
Objectives: Excess body weight and weight misperception in adolescents are associated with various physical and mental health risks. This study analysed trends in overweight, obesity, body image, and …
Objectives: Excess body weight and weight misperception in adolescents are associated with various physical and mental health risks. This study analysed trends in overweight, obesity, body image, and body weight perception among Czech adolescents between 2002 and 2022, considering gender, age and socioeconomic status (SES). Methods: Data were retrieved from the questionnaire of the Health Behaviour in School-aged Children (HBSC) study conducted in 2002, 2006, 2010, 2014, 2018 and 2022 (n=52,363; 49.9% girls). The Difference test between two proportions was used to assess time trends in weight status (WS), body image, and body weight perception across gender and SES groups. Logistic regression analysis was performed to examine the likelihood of being overweight/obese, and underestimating or overestimating WS. Results: Between 2002 and 2022, overweight and obesity increased significantly, while non-overweight rates declined across both genders and SES groups, with a greater rise among boys and adolescents from low SES backgrounds. In 2022, more adolescents, regardless the gender and SES, perceived their body as “too thin” compared to 2002. Over the 20-year period, underestimation of WS increased while overestimation decreased among both girls and boys and across all SES groups. Accurate perception of WS rose among girls but worsened among boys. Girls were less likely than boys to be overweight/obese or to underestimate their WS but had higher odds of overestimating it. Conclusions: The significant rise in overweight and obesity, especially in boys and adolescents from low SES backgrounds, during the last 20 years points out to socio-economic disparities and should be taken into account when creating new policies. An improvement in correct perception of WS among girls and a decline in overestimating WS across both genders and SES groups, could help reduce the risks of developing mental health problems or eating disorders, whereas underestimating WS may lead to weight-related issues.
Trends in Active School Transport Among Czech Adolescents Between 2006–2022: Findings from the HBSC Study
Objectives: Active school transport (AST), such as walking or cycling to and from school, represents an important source of daily physical activity for adolescents. In recent decades, however, many hi…
Objectives: Active school transport (AST), such as walking or cycling to and from school, represents an important source of daily physical activity for adolescents. In recent decades, however, many high-income countries have reported a steady decline in AST. The main objective of this study was to describe long-term trends in active travel to and from school among Czech adolescents aged 11, 13, and 15 years, using nationally representative data collected in five waves of the Health Behaviour in School-aged Children (HBSC) study between 2006 and 2022. A secondary aim was to explore selected individual and socioeconomic factors associated with AST participation. Methods: The analysis is based on a total sample of 50,713 adolescents (boys: n=25,628; girls: n=25,085) aged 10.5–16.5 years, with valid self-reported data on travel modes to and from school. AST was defined as walking or cycling as the primary mode of transport. The prevalence of AST was analyzed over time by gender and age category. Binary logistic regression was used to assess the associations between AST and survey year, gender, age group, socioeconomic status (Family Affluence Scale), and commuting time to school. Results: Between 2006 and 2022, the prevalence of AST to school declined from 71.6% to 54.9% among boys and from 71.8% to 54.8% among girls. A similar trend was observed for AST from school, although participation remained consistently higher than in the morning. The strongest negative predictors of AST were longer commuting time and higher socioeconomic status. Girls had slightly lower odds of AST than boys, and older adolescents were more likely to engage in AST.
A Novel Dataset for Streaming Learning Analytics
This research introduces a novel dataset developed for streaming learning analytics, derived from the Open University Learning Analytics Dataset (OULAD). The dataset incorporates essential temporal in…
This research introduces a novel dataset developed for streaming learning analytics, derived from the Open University Learning Analytics Dataset (OULAD). The dataset incorporates essential temporal information that captures the timing of student interactions with the Virtual Learning Environment (VLE). By integrating these time-based interactions, the dataset enhances the capabilities of stream algorithms, which are particularly well-suited for real-time monitoring and analysis of student learning behaviors. The dataset consists of 34 features and 1,718,983 samples, encompassing students' demographic information, assessment scores, and interactions with the VLE for a specific time ( T ), corresponding to each student ( S ) within a given course ( C ) and module ( M ). The target classes—'Withdrawn', 'Fail', 'Pass', and 'Distinction'—were encoded as 0, 1, 2, and 3, respectively. Notably, the data exhibits a significant imbalance, with a substantial prevalence of records associated with students who passed the final examination. The class distribution is as follows: 'Pass' (1,022,760 samples), 'Distinction' (308,642 samples), 'Fail' (227,550$ samples), and 'Withdrawn' (160,031 samples).
The fusion of hyperparameter candidates for one-class classification problems
Refining effect size measures and classification for differential item functioning: Toward unified guidelines across methods
Differential Item Functioning (DIF) analysis is used to identify potentially biased items in multi-item measurements. In addition to testing the statistical significance, it is essential to evaluate t…
Differential Item Functioning (DIF) analysis is used to identify potentially biased items in multi-item measurements. In addition to testing the statistical significance, it is essential to evaluate the practical significance of DIF through effect size measures. We review existing DIF effect size measures and cut-off values used to classify the effect size magnitudes for the Mantel-Haenszel test, SIBTEST, and logistic regression for binary items, and introduce a refinement of area-based effect size measures. A simulation study is conducted to investigate the properties of these effect size measures and existing classification guidelines, and to assess their comparative performance. The results indicate that some commonly used effect size measures exhibit undesirable properties, including inconsistent classifications, systematic underestimation of the magnitude of the underlying DIF, and strong dependence on design factors. To address these issues, we introduce usage restrictions for some effect size measures, revised cut-off values that unify results across different methods, and propose new cut-off values for area-based effect size measures. The methods are demonstrated using two real data examples. Implementation is provided in the R software.
Physical Activity and Life Satisfaction Data from the 4HAIE Study for Modelling Nonlinear Dynamics
This repository contains the dataset and accompanying code used in the manuscript: "Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data." …
This repository contains the dataset and accompanying code used in the manuscript: "Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data." The dataset originates from the 4HAIE (Healthy Aging in Industrial Environment - Program 4) study, a 12-month intensive longitudinal study designed to examine within-person dynamics of health behaviors and well-being in everyday life (see corresponding protocol paper Elavsky et al., 2021). Dataset Contents The repository includes: Merged analysis dataset linking daily physical activity with corresponding end-of-day life satisfaction assessments. Daily physical activity data collected continuously over approximately 12 months using Fitbit Charge 3 and Fitbit Charge 4 devices. Physical activity is represented by daily step counts. Ecological Momentary Assessment (EMA) data collected during four 14-day measurement bursts distributed across the 12-month study period. Participants completed an end-of-day survey assessing life satisfaction using two items adapted from the Satisfaction with Life Scale (SWLS). Illustrative analysis code demonstrating the generalized additive modeling (GAM) approach used to investigate nonlinear relationships between physical activity and life satisfaction over time. Study Design Participants were monitored continuously for physical activity throughout the study while completing repeated intensive EMA assessments during four separate two-week measurement bursts. This design enabled examination of both long-term behavioral patterns and short-term fluctuations in subjective well-being within individuals. Variables The repository includes variables related to: Participant identifier (de-identified) Date of observation Daily step count (Fitbit) End-of-day life satisfaction ratings (two EMA items) Additional temporal variables required for longitudinal modeling Variable definitions and coding information are provided in the accompanying documentation. Purpose The dataset is intended to illustrate statistical approaches for analyzing intensive longitudinal health behavior data, with particular emphasis on nonlinear modeling of within-person associations using generalized additive models (GAMs). Rather than serving solely as an empirical example, the dataset demonstrates a methodological framework for capturing complex temporal relationships that may not be adequately represented using traditional linear approaches. Associated Publication This dataset accompanies the manuscript: Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data. If you use this dataset or the accompanying analysis code, please cite both this Zenodo repository and the associated publication. References: ELavsky, S., Brabec, M., Maly, M. (in review). Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data. Elavsky S, Jandačková V, Knapová L, Vašendová V, Sebera M, Kaštovská B, Blaschová D, Kühnová J, Cimler R, Vilímek D, Bosek T, Koenig J, Jandačka D. Physical activity in an air-polluted environment: behavioral, psychological and neuroimaging protocol for a prospective cohort study (Healthy Aging in Industrial Environment study - Program 4). BMC Public Health. 2021 Jan 12;21(1):126. doi: 10.1186/s12889-021-10166-4. PMID: 33435943; PMCID: PMC7801866. Elavsky S, Brabec M, Maly M, Knapova L, Kastovska B, Sebera M, Ely M, Jandackova VK, Keller J, Pavel M. The temporal dynamics of the association between daily physical activity and life satisfaction. Ann Behav Med. 2025 Jan 4;59(1):kaaf079. doi: 10.1093/abm/kaaf079. PMID: 41224246; PMCID: PMC12757008.
Pilot Assessment of Transparency of LLM-based Systems to Support Emergency Rooms
One of the main challenges when developing medical decision support systems for the emergency room is adequately filtering the most relevant information. High workload, stress, and the necessity for u…
One of the main challenges when developing medical decision support systems for the emergency room is adequately filtering the most relevant information. High workload, stress, and the necessity for urgent decisions require precise answers to the questions posed. Although LLM-based systems can provide abundant information, physicians need concise and relevant data in this particular clinical setting. In this study, we perform a pilot assessment of the transparency of selected LLM-based systems. The comparative analysis includes ChatGPT o1 model, which was asked to produce responses with varying temperatures and a pilot graph-based RAG specializing in cardiovascular diseases. A survey was conducted among 33 clinicians regarding the amount of information contained in the provided prompts. Physicians favored the most readable, specific, and helpful answers in emergency department conditions. Reliable medical data and the form in which answers are delivered are crucial for physicians working in the emergency room. We conclude that physicians have preferences for LLM responses at a specific temperature. Further research should be expanded to enable tailoring responses not only to the clinical situation but also to the experience of the asking physician.
Size-free one-class classification approach using probabilistic local cluster balance
Evolving fuzzy classification for human-centered explainable learning analytics in virtual environments
This study explores the use of explainable artificial intelligence in education, with a focus on its relevance for Learning Analytics. The research introduces a prototype-based dynamic incremental cla…
This study explores the use of explainable artificial intelligence in education, with a focus on its relevance for Learning Analytics. The research introduces a prototype-based dynamic incremental classification algorithm, Dynamic Incremental Semi-Supervised Fuzzy C-Means (DISSFCM), which leverages fuzzy logic to analyze student interaction data from virtual learning platforms, even when the data are only partially labeled. The proposed methodology generates human-centered explanations by extracting IF-THEN fuzzy rules from the evolving prototypes produced by DISSFCM over successive time intervals. These explanations, expressed in linguistic terms, remain accessible to non-expert stakeholders and are particularly suitable for educational contexts. The Open University Learning Analytics Dataset (OULAD) is utilized for experimentation and validation, providing a realistic scenario for semi-supervised data collection. Visual summaries of the evolving fuzzy rules support the identification of temporal patterns in streaming data. Results show that the model effectively adapts to concept drift while maintaining interpretability. Most notably, it proves robust in handling partially labeled data and variable time granularities, two challenges frequently encountered in real-world Learning Analytics sce- narios. The ability to both predict student outcomes and provide intelligible explanations under such constraints highlights the practical value of the approach. To evaluate the quality and relevance of the generated explanations, an expert-based evaluation was conducted. Domain experts evaluated the clarity, usefulness, and accuracy of the explanations in terms of their support for human understanding and decision-making. The results suggest that the explanations were perceived as generally informative and useful, supporting the method’s relevance for human-centered educational applications.
Incremental learning and granular computing from evolving data streams: An application to speech-based bipolar disorder diagnosis
We apply an evolving granular-computing modeling approach, called evolving Optimal Granular System (eOGS), to bipolar mood disorder (BD) diagnosis based on speech data streams. The eOGS online learnin…
We apply an evolving granular-computing modeling approach, called evolving Optimal Granular System (eOGS), to bipolar mood disorder (BD) diagnosis based on speech data streams. The eOGS online learning algorithm reveals information granules in the flow and design the structure and parameters of a granular rule-based model with a certain degree of interpretability based on acoustic attributes obtained from phone calls made over 7 months to the Psychiatry department of a hospital. A multi-objective programming problem that trades-off information specificity, model compactness, and numerical and granular error indices is presented. Spectral and prosodic attributes are ranked and selected based on a hybrid Pearson-Spearman correlation coefficient. Low attribute-class correlation, ranging from 0.03 to 0.07, is observed, as well as high class overlap, which is typical in the psychiatric field. eOGS models for BD recognition overcome alternative computational-intelligence models, namely, Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) and Fuzzy-set-Based evolving Modeling (FBeM-Gauss), by a small margin in both best and average cases; followed by eXtended Takagi-Sugeno (xTS) and evolving Takagi Sugeno (eTS) types of models. The proposed eOGS model using only 8 of the original acoustic attributes, and about 15 ‘If-Then’ inference rules, has exhibited the best root mean square error, 0.1361, and 91.8% accuracy in sharp BD class estimates. Granules associated to linguistic labels and a granular input-output map offer human understandability with relation to the inherent process of generating class estimates. Linguistically readable eOGS rules may assist physicians in explaining symptoms and making a diagnosis.
On Solvability Degree of Systems of Partial Fuzzy Relational Equations
Systems of partial fuzzy relational equations employing undefined values in the antecedents and consequents have been approached recently. The primary focus was on the issues of sufficient solvability…
Systems of partial fuzzy relational equations employing undefined values in the antecedents and consequents have been approached recently. The primary focus was on the issues of sufficient solvability and solvability criteria. This study introduces another perspective, investigating the behavior of solvability degrees of these systems. We employ operations from the Lower estimation and Dragonfly partial algebras developed in the partial fuzzy set theory framework. Initially, we establish a degree of solvability in an appropriate space of approximations containing potential solutions for the systems. Subsequently, we introduce the concept of the alpha-lift for a given partial fuzzy set and provide its fundamental properties. This concept is employed to modify the antecedents and consequents of a given system of partial fuzzy relational equations, resulting in a modified system. The solvability degree of this modified system is then studied, and we demonstrate that, under sufficient conditions, it significantly enhances the solvability degree of the initial system. This positive impact is observed in the Godel algebra, the underlying algebraic structure of partial algebras. In conclusion, we provide illustrative examples that effectively demonstrate the theoretical results.
nuggets: Data Pattern Extraction Framework in R
nuggets is a framework for subgroup discovery, contrast and emerging patterns, association rules, and more. Developed as a package for the R statistical environment, nuggets provides a novel and …
nuggets is a framework for subgroup discovery, contrast and emerging patterns, association rules, and more. Developed as a package for the R statistical environment, nuggets provides a novel and extensible toolkit for performing rule-based analyses. Both crisp (Boolean) and fuzzy data are supported. The package generates conditions in the form of elementary conjunctions, evaluates them on a dataset, and checks the induced sub-data for interesting statistical properties. A user defined function may be evaluated on generated sub-dataset, which provides a novel generality. The aim of this paper is to present that free software to the soft computing community, as the tool could be useful to both researchers and analysts in the domain of pattern mining, as, besides searching for various existing pattern types, brand new ideas may be easily implemented and evaluated within that framework.
Positive body image is a pathway between nature contact and life satisfaction across 58 nations
When minorities clash: The role of intergroup contact, threat, and perceived discrimination in mutual attitudes of the Roma and Ukrainian Refugees
Emotional Similarity Between Immigrants and Host Societies: The Link to Intergroup Relations and Well-Being
The network dynamics of antiprejudice norms: A fieldexperiment testing antiprejudice interventions inreal groups
This dataset contains information on students attending a psychology program. There are two tracks, one national and one international. The data also contains social network data, representing their r…
This dataset contains information on students attending a psychology program. There are two tracks, one national and one international. The data also contains social network data, representing their relationships. Students were subjected to a network experiment described in the article. The data belong to the following publications:Long, F., Scheepers, D., Zingora, T., & Pliskin, R. (2025). The network dynamics of antiprejudice norms: A field experiment testing antiprejudice interventions in real groups. Political Psychology, 00, 1–26. https://doi.org/10.1111/pops.70029
Refining effect size measures and classification for differential item functioning: Toward unified guidelines across methods
Differential Item Functioning (DIF) analysis is used to identify potentially biased items in multi-item measurements. In addition to testing the statistical significance, it is essential to evaluate t…
Differential Item Functioning (DIF) analysis is used to identify potentially biased items in multi-item measurements. In addition to testing the statistical significance, it is essential to evaluate the practical significance of DIF through effect size measures. We review existing DIF effect size measures and cut-off values used to classify the effect size magnitudes for the Mantel-Haenszel test, SIBTEST, and logistic regression for binary items, and introduce a refinement of area-based effect size measures. A simulation study is conducted to investigate the properties of these effect size measures and existing classification guidelines, and to assess their comparative performance. The results indicate that some commonly used effect size measures exhibit undesirable properties, including inconsistent classifications, systematic underestimation of the magnitude of the underlying DIF, and strong dependence on design factors. To address these issues, we introduce usage restrictions for some effect size measures, revised cut-off values that unify results across different methods, and propose new cut-off values for area-based effect size measures. The methods are demonstrated using two real data examples. Implementation is provided in the R software.
Physical Activity and Life Satisfaction Data from the 4HAIE Study for Modelling Nonlinear Dynamics
This repository contains the dataset and accompanying code used in the manuscript: "Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data." …
This repository contains the dataset and accompanying code used in the manuscript: "Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data." The dataset originates from the 4HAIE (Healthy Aging in Industrial Environment - Program 4) study, a 12-month intensive longitudinal study designed to examine within-person dynamics of health behaviors and well-being in everyday life (see corresponding protocol paper Elavsky et al., 2021). Dataset Contents The repository includes: Merged analysis dataset linking daily physical activity with corresponding end-of-day life satisfaction assessments. Daily physical activity data collected continuously over approximately 12 months using Fitbit Charge 3 and Fitbit Charge 4 devices. Physical activity is represented by daily step counts. Ecological Momentary Assessment (EMA) data collected during four 14-day measurement bursts distributed across the 12-month study period. Participants completed an end-of-day survey assessing life satisfaction using two items adapted from the Satisfaction with Life Scale (SWLS). Illustrative analysis code demonstrating the generalized additive modeling (GAM) approach used to investigate nonlinear relationships between physical activity and life satisfaction over time. Study Design Participants were monitored continuously for physical activity throughout the study while completing repeated intensive EMA assessments during four separate two-week measurement bursts. This design enabled examination of both long-term behavioral patterns and short-term fluctuations in subjective well-being within individuals. Variables The repository includes variables related to: Participant identifier (de-identified) Date of observation Daily step count (Fitbit) End-of-day life satisfaction ratings (two EMA items) Additional temporal variables required for longitudinal modeling Variable definitions and coding information are provided in the accompanying documentation. Purpose The dataset is intended to illustrate statistical approaches for analyzing intensive longitudinal health behavior data, with particular emphasis on nonlinear modeling of within-person associations using generalized additive models (GAMs). Rather than serving solely as an empirical example, the dataset demonstrates a methodological framework for capturing complex temporal relationships that may not be adequately represented using traditional linear approaches. Associated Publication This dataset accompanies the manuscript: Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data. If you use this dataset or the accompanying analysis code, please cite both this Zenodo repository and the associated publication. References: ELavsky, S., Brabec, M., Maly, M. (in review). Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data. Elavsky S, Jandačková V, Knapová L, Vašendová V, Sebera M, Kaštovská B, Blaschová D, Kühnová J, Cimler R, Vilímek D, Bosek T, Koenig J, Jandačka D. Physical activity in an air-polluted environment: behavioral, psychological and neuroimaging protocol for a prospective cohort study (Healthy Aging in Industrial Environment study - Program 4). BMC Public Health. 2021 Jan 12;21(1):126. doi: 10.1186/s12889-021-10166-4. PMID: 33435943; PMCID: PMC7801866. Elavsky S, Brabec M, Maly M, Knapova L, Kastovska B, Sebera M, Ely M, Jandackova VK, Keller J, Pavel M. The temporal dynamics of the association between daily physical activity and life satisfaction. Ann Behav Med. 2025 Jan 4;59(1):kaaf079. doi: 10.1093/abm/kaaf079. PMID: 41224246; PMCID: PMC12757008.
Enhancing Psychometric Analysis with Interactive SIA Modules
ShinyItemAnalysis (SIA) is an R package and shiny application for an interactive presentation of psychometric methods and analysis of multi-item measurements in psychology, education, and social scien…
ShinyItemAnalysis (SIA) is an R package and shiny application for an interactive presentation of psychometric methods and analysis of multi-item measurements in psychology, education, and social sciences in general. In this article, we present a new feature introduced in the recent version of the package, called "SIA modules," which allows researchers and practitioners to offer new analytical methods for broader use via add-on extensions. SIA modules are designed to integrate with and build upon the SIA interactive application, enabling them to leverage the existing infrastructure for tasks, such as data uploading and processing. They can access and further use a range of outputs from various analyses, including models and datasets. Because SIA modules come in R packages (or extend existing ones), they can be bundled with their datasets, utilize object-oriented systems, or even compiled code. We illustrate the concepts using sample modules from the newly introduced SIAmodules package and other packages. After providing a general overview of building Shiny applications, we describe how to develop the SIA add-on modules with the support of the new SIAtools package. Finally, we discuss the possibilities of future development and emphasize the importance of freely available, interactive psychometric software for disseminating methodological innovations.
Adaptive exact recovery in sparse nonparametric models
We observe an unknown function of d variables f(t), t ∈ [0, 1]^d, in the Gaussian white noise model of intensity ε > 0. We assume that the function f is regular and that it is a sum of…
We observe an unknown function of d variables f(t), t ∈ [0, 1]^d, in the Gaussian white noise model of intensity ε > 0. We assume that the function f is regular and that it is a sum of k-variate functions, where k varies from 1 to s (1 ≤ s ≤ d). These functions are unknown to us and only a few of them are nonzero. In this article, we address the problem of identifying the nonzero components of f in the case when d = d_ε → ∞ as ε → 0 and s is either fixed or s = s_ε → ∞, s = o(d) as ε → ∞. This may be viewed as a variable selection problem. We derive the conditions when exact variable selection in the model at hand is possible and provide a selection procedure that achieves this type of selection. The procedure is adaptive to a degree of model sparsity described by the sparsity parameter β ∈ (0, 1). We also derive conditions that make the exact variable selection impossible. Our results augment previous work in this area.
Block-Coordinate Descent Algorithm for Interventional Data in Directed Graphical Models
Computing maximum likelihood estimates in linear structural equation models is generally a difficult problem. The critical equations are usually non-linear and have numerous solutions, even for purely…
Computing maximum likelihood estimates in linear structural equation models is generally a difficult problem. The critical equations are usually non-linear and have numerous solutions, even for purely observational data. The block-coordinate descent (BCD) algorithm proposed by Drton et al. (2019)[1] is an efficient way to solve the optimization problem by decomposing it into a series of sub-problems with closed-form solutions, and which works with observational data. In this work, we describe the general problem of a BCD-type scheme for computing maximum likelihood estimates in linear structural equation models without hidden variables, integrating multiple observational and interventional environments. With interventional data, the degrees of both the original likelihood equations and the block-coordinate update equations could increase greatly. We study special setups in which the block optimization subproblems have a degree of at most 2 and provide closed-form solutions in these cases. Additionally, we discuss the potential applications of the model and algorithm to health and well-being data.
Assessing quality of selection procedures: Lower bound of false positive rate as a function of inter-rater reliability
Inter-rater reliability (IRR) is one of the commonly used tools for assessing the quality of ratings from multiple raters. However, applicant selection procedures based on ratings from multiple raters…
Inter-rater reliability (IRR) is one of the commonly used tools for assessing the quality of ratings from multiple raters. However, applicant selection procedures based on ratings from multiple raters usually result in a binary outcome - the applicant is either selected or not. This final outcome is not considered in IRR, which instead focuses on the ratings of the individual subjects or objects. We outline the connection between the ratings' measurement model (used for IRR) and a binary classification framework. We develop a simple way of approximating the probability of correctly selecting the best applicants which allows us to compute error probabilities of the selection procedure (i.e., false positive and false negative rate) or their lower bounds. We draw connections between the IRR and the binary classification metrics, showing that binary classification metrics depend solely on the IRR coefficient and proportion of selected applicants. We assess the performance of the approximation in a simulation study and apply it in an example comparing the reliability of multiple grant peer review selection procedures. We also discuss other possible uses of the explored connections in other contexts, such as educational testing, psychological assessment, and health-related measurement, and implement the computations in the R package IRR2FPR.
Improvements in Upper Extremity Isometric Muscle Strength, Dexterity, and Self-Care Independence During the Sub-Acute Phase of Stroke Recovery: An Observational Study on the Effects of Intensive Comprehensive Rehabilitation.
BACKGROUND: Stroke often impairs upper extremity motor function, with recovery in the sub-acute phase being crucial for regaining independence. This study examines changes in isometric muscle strength…
BACKGROUND: Stroke often impairs upper extremity motor function, with recovery in the sub-acute phase being crucial for regaining independence. This study examines changes in isometric muscle strength, dexterity, and self-care independence during this period, and evaluates the effects of a comprehensive intensive rehabilitation (COMIRESTROKE). METHODS: Individuals in sub-acute stroke recovery and age- and sex-matched controls were assessed for pre- and post-rehabilitation differences in primary outcomes (grip/pinch strength, Nine Hole Peg Test [NHPT], Action Research Arm Test [ARAT]). COMIRESTROKE’s effects on primary and secondary outcomes (National Institute of Health Stroke Scale [NIHSS], Modified Rankin Scale [MRS], Functional Independence Measure [FIM]) were evaluated. Outcomes were analyzed for dominant and non-dominant limbs, both regardless of impairment and with a focus on impaired limbs. RESULTS: Fifty-two individuals with stroke (NIHSS 7.51 ± 5.71, age 70.25 ± 12.66 years, 21.36 ± 12.06 days post-stroke) and forty-six controls participated. At baseline, individuals with stroke showed significantly lower strength (dominant grip, key pinch, tip-tip pinch, padj < 0.05), higher NHPT scores (padj < 0.05), and lower ARAT scores (padj < 0.001). COMIRESTROKE led to improvements in dominant key pinch, non-dominant tip-tip pinch, NHPT, and both dominant and non-dominant ARAT (padj < 0.05). Notably, non-dominant key pinch improved significantly when considering only impaired hands. Pre- and post-test differences between groups were significant only for ARAT (both limbs), even after adjustment (padj < 0.05). All secondary outcomes (NIHSS, MRS, FIM) showed significant improvement post-COMIRESTROKE (padj < 0.001). CONCLUSION: Individuals with stroke exhibit reduced muscle strength and dexterity, impairing independence. However, comprehensive intensive rehabilitation significantly improves these functions. Data are available from the corresponding author upon request and are part of a sub-study of NCT05323916.
Zpráva z Mezinárodní konference Psychometrické společnosti (IMPS 2024), Praha, 15.−19. července 2024
Výroční Mezinárodní konferenci Psychometrické společnosti (International Meeting of Psychometric Society, IMPS 2024) ve dnech 15.−19. července 2024 hostil &Uac…
Výroční Mezinárodní konferenci Psychometrické společnosti (International Meeting of Psychometric Society, IMPS 2024) ve dnech 15.−19. července 2024 hostil Ústav informatiky Akademie věd České republiky (AV ČR) ve spolupráci s Pedagogickou fakultou UK (PedF UK) a Vysokou školou ekonomickou (VŠE) v Praze. V rámci Ústavu informatiky AV ČR konferenci pořádala Skupina výpočetní psychometrie, která se zabývá výpočetními aspekty, vývojem a aplikací statistických metod pro analýzu měření v psychologii, vzdělávání a dalších sociálních vědách. Na VŠE organizaci podpořili členové fakulty informatiky a statistiky. Z PedF UK se zapojili členové Ústavu výzkumu a rozvoje vzdělávání, který se zaobírá mj. analýzou dat z velkých mezinárodních studií.The annual International Meeting of the Psychometric Society (IMPS 2024), held from July 15–19, 2024, was hosted by the Institute of Computer Science of the Czech Academy of Sciences (CAS) in cooperation with the Faculty of Education at Charles University (PedF UK) and the University of Economics (VŠE) in Prague. Within the Institute of Computer Science at CAS, the conference was organized by the Computational Psychometrics Group, which focuses on the computational aspects, development, and application of statistical methods for measurement analysis in psychology, education, and other social sciences. The Faculty of Informatics and Statistics at the Prague University of Economics and Business also provided organizational support. From the Faculty of Education at Charles University, members of the Institute for Research and Development in Education, which specializes in analyzing data from large international studies, participated in the event organization.
New iterative algorithms for estimation of item functioning
This article explores innovations for parameter estimation in generalized linear and nonlinear models, which may be used in item response modeling to account for guessing/pretending or slipping/dissim…
This article explores innovations for parameter estimation in generalized linear and nonlinear models, which may be used in item response modeling to account for guessing/pretending or slipping/dissimulation and for the effect of covariates. We introduce a new implementation of the EM algorithm and propose a new algorithm based on the parametrized link function. The two novel iterative algorithms are compared to existing methods in a simulation study. Additionally, the study examines software implementation, including the specification of initial values for numerical algorithms and asymptotic properties with an estimation of standard errors. Overall, the newly proposed algorithm based on the parametrized link function outperforms other procedures, especially for small sample sizes. Moreover, the newly implemented EM algorithm provides additional information regarding respondents’ inclination to guess or pretend and slip or dissimulate when answering the item. The study also discusses applications of the methods in the context of the detection of differential item functioning and addresses the measurement error. Methods are offered in the difNLR package and in the interactive application of the ShinyItemAnalysis package; demonstration is provided using real data from psychological and educational assessments.
A Novel Dataset for Streaming Learning Analytics
This research introduces a novel dataset developed for streaming learning analytics, derived from the Open University Learning Analytics Dataset (OULAD). The dataset incorporates essential temporal in…
This research introduces a novel dataset developed for streaming learning analytics, derived from the Open University Learning Analytics Dataset (OULAD). The dataset incorporates essential temporal information that captures the timing of student interactions with the Virtual Learning Environment (VLE). By integrating these time-based interactions, the dataset enhances the capabilities of stream algorithms, which are particularly well-suited for real-time monitoring and analysis of student learning behaviors. The dataset consists of 34 features and 1,718,983 samples, encompassing students' demographic information, assessment scores, and interactions with the VLE for a specific time ( T ), corresponding to each student ( S ) within a given course ( C ) and module ( M ). The target classes—'Withdrawn', 'Fail', 'Pass', and 'Distinction'—were encoded as 0, 1, 2, and 3, respectively. Notably, the data exhibits a significant imbalance, with a substantial prevalence of records associated with students who passed the final examination. The class distribution is as follows: 'Pass' (1,022,760 samples), 'Distinction' (308,642 samples), 'Fail' (227,550$ samples), and 'Withdrawn' (160,031 samples).
Physical Activity and Life Satisfaction Data from the 4HAIE Study for Modelling Nonlinear Dynamics
This repository contains the dataset and accompanying code used in the manuscript: "Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data." …
This repository contains the dataset and accompanying code used in the manuscript: "Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data." The dataset originates from the 4HAIE (Healthy Aging in Industrial Environment - Program 4) study, a 12-month intensive longitudinal study designed to examine within-person dynamics of health behaviors and well-being in everyday life (see corresponding protocol paper Elavsky et al., 2021). Dataset Contents The repository includes: Merged analysis dataset linking daily physical activity with corresponding end-of-day life satisfaction assessments. Daily physical activity data collected continuously over approximately 12 months using Fitbit Charge 3 and Fitbit Charge 4 devices. Physical activity is represented by daily step counts. Ecological Momentary Assessment (EMA) data collected during four 14-day measurement bursts distributed across the 12-month study period. Participants completed an end-of-day survey assessing life satisfaction using two items adapted from the Satisfaction with Life Scale (SWLS). Illustrative analysis code demonstrating the generalized additive modeling (GAM) approach used to investigate nonlinear relationships between physical activity and life satisfaction over time. Study Design Participants were monitored continuously for physical activity throughout the study while completing repeated intensive EMA assessments during four separate two-week measurement bursts. This design enabled examination of both long-term behavioral patterns and short-term fluctuations in subjective well-being within individuals. Variables The repository includes variables related to: Participant identifier (de-identified) Date of observation Daily step count (Fitbit) End-of-day life satisfaction ratings (two EMA items) Additional temporal variables required for longitudinal modeling Variable definitions and coding information are provided in the accompanying documentation. Purpose The dataset is intended to illustrate statistical approaches for analyzing intensive longitudinal health behavior data, with particular emphasis on nonlinear modeling of within-person associations using generalized additive models (GAMs). Rather than serving solely as an empirical example, the dataset demonstrates a methodological framework for capturing complex temporal relationships that may not be adequately represented using traditional linear approaches. Associated Publication This dataset accompanies the manuscript: Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data. If you use this dataset or the accompanying analysis code, please cite both this Zenodo repository and the associated publication. References: ELavsky, S., Brabec, M., Maly, M. (in review). Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data. Elavsky S, Jandačková V, Knapová L, Vašendová V, Sebera M, Kaštovská B, Blaschová D, Kühnová J, Cimler R, Vilímek D, Bosek T, Koenig J, Jandačka D. Physical activity in an air-polluted environment: behavioral, psychological and neuroimaging protocol for a prospective cohort study (Healthy Aging in Industrial Environment study - Program 4). BMC Public Health. 2021 Jan 12;21(1):126. doi: 10.1186/s12889-021-10166-4. PMID: 33435943; PMCID: PMC7801866. Elavsky S, Brabec M, Maly M, Knapova L, Kastovska B, Sebera M, Ely M, Jandackova VK, Keller J, Pavel M. The temporal dynamics of the association between daily physical activity and life satisfaction. Ann Behav Med. 2025 Jan 4;59(1):kaaf079. doi: 10.1093/abm/kaaf079. PMID: 41224246; PMCID: PMC12757008.
Pilot Assessment of Transparency of LLM-based Systems to Support Emergency Rooms
One of the main challenges when developing medical decision support systems for the emergency room is adequately filtering the most relevant information. High workload, stress, and the necessity for u…
One of the main challenges when developing medical decision support systems for the emergency room is adequately filtering the most relevant information. High workload, stress, and the necessity for urgent decisions require precise answers to the questions posed. Although LLM-based systems can provide abundant information, physicians need concise and relevant data in this particular clinical setting. In this study, we perform a pilot assessment of the transparency of selected LLM-based systems. The comparative analysis includes ChatGPT o1 model, which was asked to produce responses with varying temperatures and a pilot graph-based RAG specializing in cardiovascular diseases. A survey was conducted among 33 clinicians regarding the amount of information contained in the provided prompts. Physicians favored the most readable, specific, and helpful answers in emergency department conditions. Reliable medical data and the form in which answers are delivered are crucial for physicians working in the emergency room. We conclude that physicians have preferences for LLM responses at a specific temperature. Further research should be expanded to enable tailoring responses not only to the clinical situation but also to the experience of the asking physician.
Evolving fuzzy classification for human-centered explainable learning analytics in virtual environments
This study explores the use of explainable artificial intelligence in education, with a focus on its relevance for Learning Analytics. The research introduces a prototype-based dynamic incremental cla…
This study explores the use of explainable artificial intelligence in education, with a focus on its relevance for Learning Analytics. The research introduces a prototype-based dynamic incremental classification algorithm, Dynamic Incremental Semi-Supervised Fuzzy C-Means (DISSFCM), which leverages fuzzy logic to analyze student interaction data from virtual learning platforms, even when the data are only partially labeled. The proposed methodology generates human-centered explanations by extracting IF-THEN fuzzy rules from the evolving prototypes produced by DISSFCM over successive time intervals. These explanations, expressed in linguistic terms, remain accessible to non-expert stakeholders and are particularly suitable for educational contexts. The Open University Learning Analytics Dataset (OULAD) is utilized for experimentation and validation, providing a realistic scenario for semi-supervised data collection. Visual summaries of the evolving fuzzy rules support the identification of temporal patterns in streaming data. Results show that the model effectively adapts to concept drift while maintaining interpretability. Most notably, it proves robust in handling partially labeled data and variable time granularities, two challenges frequently encountered in real-world Learning Analytics sce- narios. The ability to both predict student outcomes and provide intelligible explanations under such constraints highlights the practical value of the approach. To evaluate the quality and relevance of the generated explanations, an expert-based evaluation was conducted. Domain experts evaluated the clarity, usefulness, and accuracy of the explanations in terms of their support for human understanding and decision-making. The results suggest that the explanations were perceived as generally informative and useful, supporting the method’s relevance for human-centered educational applications.
Incremental learning and granular computing from evolving data streams: An application to speech-based bipolar disorder diagnosis
We apply an evolving granular-computing modeling approach, called evolving Optimal Granular System (eOGS), to bipolar mood disorder (BD) diagnosis based on speech data streams. The eOGS online learnin…
We apply an evolving granular-computing modeling approach, called evolving Optimal Granular System (eOGS), to bipolar mood disorder (BD) diagnosis based on speech data streams. The eOGS online learning algorithm reveals information granules in the flow and design the structure and parameters of a granular rule-based model with a certain degree of interpretability based on acoustic attributes obtained from phone calls made over 7 months to the Psychiatry department of a hospital. A multi-objective programming problem that trades-off information specificity, model compactness, and numerical and granular error indices is presented. Spectral and prosodic attributes are ranked and selected based on a hybrid Pearson-Spearman correlation coefficient. Low attribute-class correlation, ranging from 0.03 to 0.07, is observed, as well as high class overlap, which is typical in the psychiatric field. eOGS models for BD recognition overcome alternative computational-intelligence models, namely, Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) and Fuzzy-set-Based evolving Modeling (FBeM-Gauss), by a small margin in both best and average cases; followed by eXtended Takagi-Sugeno (xTS) and evolving Takagi Sugeno (eTS) types of models. The proposed eOGS model using only 8 of the original acoustic attributes, and about 15 ‘If-Then’ inference rules, has exhibited the best root mean square error, 0.1361, and 91.8% accuracy in sharp BD class estimates. Granules associated to linguistic labels and a granular input-output map offer human understandability with relation to the inherent process of generating class estimates. Linguistically readable eOGS rules may assist physicians in explaining symptoms and making a diagnosis.
On Solvability Degree of Systems of Partial Fuzzy Relational Equations
Systems of partial fuzzy relational equations employing undefined values in the antecedents and consequents have been approached recently. The primary focus was on the issues of sufficient solvability…
Systems of partial fuzzy relational equations employing undefined values in the antecedents and consequents have been approached recently. The primary focus was on the issues of sufficient solvability and solvability criteria. This study introduces another perspective, investigating the behavior of solvability degrees of these systems. We employ operations from the Lower estimation and Dragonfly partial algebras developed in the partial fuzzy set theory framework. Initially, we establish a degree of solvability in an appropriate space of approximations containing potential solutions for the systems. Subsequently, we introduce the concept of the alpha-lift for a given partial fuzzy set and provide its fundamental properties. This concept is employed to modify the antecedents and consequents of a given system of partial fuzzy relational equations, resulting in a modified system. The solvability degree of this modified system is then studied, and we demonstrate that, under sufficient conditions, it significantly enhances the solvability degree of the initial system. This positive impact is observed in the Godel algebra, the underlying algebraic structure of partial algebras. In conclusion, we provide illustrative examples that effectively demonstrate the theoretical results.
nuggets: Data Pattern Extraction Framework in R
nuggets is a framework for subgroup discovery, contrast and emerging patterns, association rules, and more. Developed as a package for the R statistical environment, nuggets provides a novel and …
nuggets is a framework for subgroup discovery, contrast and emerging patterns, association rules, and more. Developed as a package for the R statistical environment, nuggets provides a novel and extensible toolkit for performing rule-based analyses. Both crisp (Boolean) and fuzzy data are supported. The package generates conditions in the form of elementary conjunctions, evaluates them on a dataset, and checks the induced sub-data for interesting statistical properties. A user defined function may be evaluated on generated sub-dataset, which provides a novel generality. The aim of this paper is to present that free software to the soft computing community, as the tool could be useful to both researchers and analysts in the domain of pattern mining, as, besides searching for various existing pattern types, brand new ideas may be easily implemented and evaluated within that framework.
Upper Boundary Algebra for Modeling the Missing Values Is a Residuated Lattice
It is already more than 100 years since the first proposal on three-valued logic appeared and it became a seminal work initiating lots of followers among scholars and researchers. Since then, we have …
It is already more than 100 years since the first proposal on three-valued logic appeared and it became a seminal work initiating lots of followers among scholars and researchers. Since then, we have observed distinct logical and algebraic approaches to modeling undefined values, i.e., the situation when the truth value of a given proposition is neither true nor false, but it is not defined. These various algebraic models of three-valued functionality are built to model various types of undefinedness, e.g., conceptional undefinedness, inconsistencies, indeterminable values, meaningless values, or half-true. It is not surprising that recently, these three-valued logics have been extended to partial fuzzy logics, i.e. specific many-valued logics that are extended by the dummy value * that models the undefined truth value. The algebraic structures for such logics are called partial algebras. Recently, two partial algebras, namely the Dragonfly algebra and the Lower Estimation, were both developed to capture the missing or unknown values. Their main idea consists in determining the lower boundary of the truth value of a proposition that we may guarantee after processing the operations independently on what values would replace the dummies *. Such an approach naturally leads to the consequence that the dummy * behaves as a “nearly zero” or “almost false” value. Though the application potential of such algebras in processing the missing values turned out to be very useful at some problems, it turned to be promising to consider a nearly dual approach. Such an approach should model the upper boundary idea and lead to a “nearly one” or “almost true” value. This study provides the first definition of such an algebra and investigates which of the standard properties of residuated lattices remain preserved. Unlike in the lower boundary case, we surprisingly show that in principle all of them are preserved, i.e., that the Upper Boundary algebra, though extended, remains to be the residuated lattice.
The source code of paper "Predicting Subgoals in Ricochet Robots with a Graph Neural Network"
The code used to generate the results in the paper "Predicting Subgoals in Ricochet Robots with a Graph Neural Network" is publicly available and can be accessed at [https://zenodo.org/records/2042415…
The code used to generate the results in the paper "Predicting Subgoals in Ricochet Robots with a Graph Neural Network" is publicly available and can be accessed at [https://zenodo.org/records/20424155]. This includes all scripts and relevant documentation necessary to reproduce the experiments described in the manuscript. Any additional data or materials can be made available upon reasonable request to the corresponding author.
The code for weighted quantile regression in fuzzy-probabilistic inference systems
The computational notebook for results in the papers: "On Data–Driven Fuzzy Partition in the Fuzzy–Probabilistic Inference System Framework", and "Fuzzy–Probabilistic Inference Syste…
The computational notebook for results in the papers: "On Data–Driven Fuzzy Partition in the Fuzzy–Probabilistic Inference System Framework", and "Fuzzy–Probabilistic Inference Systems Based on Piecewise Linear Weighted Quantiles". It computes and demonstrates weighted quantile regression in the framework of fuzzy-probabilistic inference systems. In addition to conventional uniform partition it constructs data driven partition that is able to capture local behaviour of data.
Usability and Feasibility of a Contrast Avoidance Model-Based Virtual Reality Protocol Designed for Generalized Anxiety Disorder
Generalized anxiety disorder (GAD) is characterized by persistent, excessive, and difficult-to-control worry. The Contrast Avoidance Model (CAM) proposes that individuals with GAD use worry to sustain…
Generalized anxiety disorder (GAD) is characterized by persistent, excessive, and difficult-to-control worry. The Contrast Avoidance Model (CAM) proposes that individuals with GAD use worry to sustain negative emotional arousal, thereby avoiding sharp negative emotional contrasts that would otherwise follow unexpected adverse events. A virtual reality (VR) protocol was developed to simulate such contrasts by alternating guided relaxation with brief anxiety-inducing scenarios (skyline plank, crowded elevator, and loose dog encounter). This study evaluated the usability and feasibility of this protocol in 20 subclinical adults aged 18–45 who met a screening threshold of GAD-7 ≥ 5, using a Meta Quest 3 headset and Polar H10 heart rate sensor. Exposure segments produced a significant decrease in RMSSD (β = −0.185, p < 0.001), consistent with reduced parasympathetic activity during exposure, whereas heart rate did not differ significantly between conditions. Subjectively, exposure increased SUDS (β = 2.23, p < 0.001) and SAM arousal (β = 1.95, p < 0.001), and decreased SAM valence (β = −2.68, p < 0.001) and dominance (β = −1.70, p = 0.005). Presence scores, cybersickness ratings, and qualitative feedback supported the usability of the protocol and identified concrete design refinements. These results support the feasibility of the protocol and provide a foundation for future controlled clinical evaluation.
Data for "Trends in adolescent cigarette smoking in Czechia: findings from the HBSC study 2014–2022"
This dataset contains aggregated prevalence estimates regarding cigarette smoking among adolescents in the Czech Republic, derived from three waves of the Health Behaviour in School-aged Children (HBS…
This dataset contains aggregated prevalence estimates regarding cigarette smoking among adolescents in the Czech Republic, derived from three waves of the Health Behaviour in School-aged Children (HBSC) study conducted in 2014, 2018, and 2022. The data represents a total sample of 29,525 respondents (14,761 boys and 14,764 girls) across three target age groups: 11, 13, and 15 years old.
Trends in alcohol use among Czech adolescents: findings from the HBSC study 2014–2022
Objectives: The present study aims to examine trends in adolescent alcohol use over the period from 2014 to 2022.Methods: Data from the last three Health Behaviour in School-aged Children (H…
Objectives: The present study aims to examine trends in adolescent alcohol use over the period from 2014 to 2022.Methods: Data from the last three Health Behaviour in School-aged Children (HBSC) surveys conducted in 2014, 2018 and 2022 were used for this study. Three measures of adolescent alcohol use have been chosen for analyses: lifetime alcohol use, last 30 days alcohol use, and repeated lifetime drunkenness. The analyses comprised calculation of period-specific prevalence estimates and testing of the significance of between-period changes using survey-adjusted logistic regression models.Results: Comparing prevalence rates between the periods, consistent decrease in adolescent alcohol use becomes apparent, particularly for drop of rates in 2018 compared to those in 2014. The corresponding data on the prevalence of lifetime alcohol use among 13-year-old boys was 59.7% in 2014 and 44.2% in 2018; and among 15-year-old boys 80.4% in 2014 and 74.9% in 2018. For 13-year-old girls, the estimated prevalence was 46.9% in 2014 and 41.1% in 2018; and for 15-year-old girls 83.7% in 2014 and 75.9% in 2018. This is the case for repeated lifetime drunkenness, and the decrease is consistent across boys and girls, as well as the respective age groups. In survey waves 2018 and 2022, we do not see a statistically significant decline, but rather a stabilisation of assessed prevalence at a level from the previous wave of the study.Conclusions: The decline in alcohol use among Czech adolescents is part of a global trend of reducing alcohol drinking among young people, on the background of social mechanisms including the change of cultural status of alcohol and changes in young people's leisure preferences.
Trends in adolescent cigarette smoking in Czechia: findings from the HBSC study 2014–2022
Objectives: Regular monitoring of health-related behaviours among vulnerable populations is of public health importance. This study examines recent trends in adolescent cigarette smoking in Czech…
Objectives: Regular monitoring of health-related behaviours among vulnerable populations is of public health importance. This study examines recent trends in adolescent cigarette smoking in Czechia following the marked changes reported in the mid-2010s.Methods: Data from three recent rounds of the Health Behaviour in School-aged Children (HBSC) study conducted in Czechia in 2014, 2018 and 2022 were analysed. Temporal trends were assessed for two indicators of adolescent cigarette use: lifetime cigarette use and cigarette use in the last 30 days. Survey-adjusted binary logistic regression models were used to test changes between survey periods. In 2022, the prevalence of electronic cigarette use was additionally estimated using the same indicators.Results: A continued decline in adolescent cigarette use was observed for both indicators, extending the downward trends reported in the mid-2010s into the 2020s. The decline was most pronounced between 2014 and 2018, with smaller but persistent decreases thereafter, particularly among older adolescents. However, the findings also highlight the substantial prevalence of electronic cigarette use. In 2022, more than one-third of 15-year-olds in Czechia reported lifetime electronic cigarette use (35.1% among boys and 36.6% among girls), and approximately one in five reported use in the last 30 days (19.6% among boys and 23.0% among girls).Conclusions: While conventional cigarette use among adolescents continues to decline, electronic cigarette use represents an important component of contemporary adolescent smoking-related behaviour. In the long term, the phenomenon of electronic cigarettes may counteract intended trends in nicotine-related harms. These findings underscore the need for continued surveillance and prevention efforts in Czechia that address both conventional and emerging smoking-related products.
Mental health stigma and its consequences: a systematic scoping review of pathways to discrimination and adverse outcomes
Current research evaluating the consequences of stigma towards people with mental illness is not nuanced in emphasizing the critical distinction between stigma as negative attitudes and discrimination…
Current research evaluating the consequences of stigma towards people with mental illness is not nuanced in emphasizing the critical distinction between stigma as negative attitudes and discrimination as harmful behaviours that limit access to services, employment, and social inclusion. Understanding these distinctions is essential for designing targeted, evidence-based universal, targeted and indicated interventions to improve the quality of life and well-being. This review evaluates the evidence on the consequences of stigma towards people with mental illness. Using PRISMA guidelines, we analysed 448 studies (294 quantitative, 154 qualitative) investigating stigma's negative outcomes. Findings were categorized into health, service use, psychosocial, economic, and structural impacts. Although stigma is consistently associated with adverse outcomes across life domains, evidence of a causal link between negative attitudes and poorer outcomes for individuals with mental disorders remains limited. Furthermore, there is a striking scarcity of research from low- and middle-income countries, with significant regional gaps, and studies addressing structural stigma embedded in societal institutions are particularly rare. Efforts to combat stigma must distinguish between attitudes and behaviours, focusing on reducing discrimination while enhancing public mental health literacy and access to effective interventions. Tackling these challenges requires a comprehensive, evidence-informed approach to improving mental health outcomes for all.
Mental health in Central and Eastern Europe: a comprehensive analysis
The post-communist WHO European region, often called Central and Eastern Europe (CEE), includes 28 countries with over 770 million people. Mental health systems remain shaped by the communist legacy o…
The post-communist WHO European region, often called Central and Eastern Europe (CEE), includes 28 countries with over 770 million people. Mental health systems remain shaped by the communist legacy of centralized institutions, a narrow biomedical focus, and neglect of social and psychological dimensions. Chronic underfunding persists, further strained by shrinking civic space in some countries and the war in Ukraine. Substantial progress has been made in the past decade, with modernization and rights-based approaches gaining ground. Yet reforms face entrenched barriers: underinvestment disproportionate to the burden; pervasive stigma, weak advocacy, and limited involvement of people with lived experience; dominance of institutional care over prevention, promotion, and community services; reliance on donor-driven projects that falter once funding ends; and human resource problems. Governance is often unstable, with low prioritization, clientelism, and personal biases undermining reforms. Research and data remain scarce, leaving systems unevaluated and vulnerable to reversal. Poor decision-making compounds these barriers: systemic missteps, driven by limited expertise, weak evidence, and personal biases, prevent resources from achieving the best possible outcomes. To move forward, CEE must integrate health, social, and education systems, secure sustainable crisis services, strengthen professional skills, involve people with lived experience, expand public mental health expertise, and, above all, commit greater and more transparent investment, closer to western European levels, if resilient and effective systems are to be built.
Generalizovaná úzkostná porucha a její léčba
Generalizovaná úzkostná porucha (GAD) se vyznačuje chronickými a nadměrnými obavami v různých aspektech každodenního života, včetně osobních a p…
Generalizovaná úzkostná porucha (GAD) se vyznačuje chronickými a nadměrnými obavami v různých aspektech každodenního života, včetně osobních a pracovních povinností, zdraví či mezilidských vztahů. Diagnostická kritéria procházejí mírnou úpravou při přechodu Mezinárodní klasifikace nemocí z verze 10 (MKN-10) na novou verzi, MKN-11. V našem článku se podíváme na změny v nové klasifikaci nemocí, která by se měla stát diagnostickým vodítkem v nadcházejících letech. Cílem tohoto článku je podrobněji prozkoumat charakteristiky generalizované úzkostné poruchy, její diagnostické výzvy a aktuální postupy v léčbě, včetně nejmodernějších přístupů. Léčba GAD obvykle zahrnuje farmakoterapii, psychoterapii nebo jejich kombinaci. Častými komplikacemi úspěšné léčby jsou vedlejší účinky léků nebo nedostatečná odpověď na léčbu až rezistence. Nadějí pro pacienty může být využití moderních technologií v léčbě generalizované úzkostné poruchy, jakými je například virtuální realita, kterou využíváme v našem centru Výzkumu virtuální reality v duševním zdraví a neurovědách v Národním ústavu duševního zdraví při léčbě úzkostných poruch, a to včetně GAD. Generalized Anxiety Disorder (GAD) is characterized by chronic and excessive worries across various aspects of daily life, including personal and work-related responsibilities, health, and interpersonal relationships. The diagnostic criteria undergo a slight modification with the transition from the 10th edition of the International classification of diseases (ICD-10) to the new version, ICD-11. This article examines the changes in the new classification, which is expected to become the diagnostic guideline in the coming years. The aim of this article is to explore in detail the characteristics of generalized anxiety disorder, its diagnostic challenges, and current treatment approaches, including the most modern techniques. GAD treatment typically involves pharmacotherapy, psychotherapy, or a combination of both. Common complications in the successful treatment of GAD include side effects of medications or insufficient response to treatment, including resistance. A promising approach for patients may be the use of modern technologies in the treatment of generalized anxiety disorder, such as virtual reality used at our Virtual reality research center for mental health and neurosciences at the National institute of mental health in the treatment of anxiety disorders, including GAD.
Supplementary data for "Changes in stigma and population mental health literacy before and after the Covid-19 pandemic: Analyses of repeated cross-sectional studies"
The data come from cross-sectional surveys conducted on representative samples of the non-institutionalised adult population in the Czech Republic in 2017, 2019 and 2022. The data include basic demogr…
The data come from cross-sectional surveys conducted on representative samples of the non-institutionalised adult population in the Czech Republic in 2017, 2019 and 2022. The data include basic demographic data and data from four questionnaires. Data on mental health problems were assessed using the Mini International Neuropsychiatric Interview (M.I.N.I.) in 2017 and 2022. The Self-identification of Mental Illness (SELF-I) scale was used to assess self-identification as having a mental illness in these years. Stigma associated with mental health was assessed using the Reported and Intended Behaviour Scale (RIBS) and the Community Attitudes towards Mental Illness (CAMI) scale in 2019 and 2022. Data pocházejí z průřezových šetření provedených na reprezentativních vzorcích neinstitucionalizované dospělé populace v České republice v letech 2017, 2019 a 2022. Data zahrnují základní demografické údaje a údaje ze čtyř dotazníků. Údaje o problémech v oblasti duševního zdraví byly hodnoceny pomocí Mini mezinárodního neuropsychiatrického rozhovoru (M.I.N.I.) v letech 2017 a 2022. K posouzení sebeidentifikace jako osoby s duševním onemocněním byla v těchto letech použita škála Self-identification of Mental Illness (SELF-I). Stigmatizace spojená s duševním zdravím byla hodnocena pomocí škály Reported and Intended Behaviour Scale (RIBS) a škály Community Attitudes towards Mental Illness (CAMI) v letech 2019 a 2022.
Excess mortality in people hospitalised for alcohol use disorders before and during the pandemic – A registry-based retrospective cohort study
Introduction The aim was to analyse mortality and estimate the life expectancy among people hospitalised for alcohol use disorders (AUD) compared with the general Czech population aged ≥20 y…
Introduction The aim was to analyse mortality and estimate the life expectancy among people hospitalised for alcohol use disorders (AUD) compared with the general Czech population aged ≥20 years. A temporal perspective on excess mortality was used, covering three recent calendar periods before and during the pandemic. Methods Three retrospective cohorts of the target population were constructed using registry-based data. The target population was defined as all adult patients (aged ≥20 years) admitted to the hospital for AUD (ICD-10 dg. of F10.x) between 2010 and 2021. Age-adjusted mortality rates and life expectancies were calculated for the comparative analysis. Official Czech mortality and vital statistics were used for the comparison. A Poisson log-linear regression model was used to test the effect of the pandemic period (2020–2021) on mortality in the AUD target population. Results At age 20, the estimated life expectancy of the AUD target was 21–27 years less than that of the Czech general population. Excess mortality was relatively highest in young people aged 20–34 years and in adults aged 35–49 years. During the pandemic period 2020–2021, mortality rates in the target AUD increased significantly. However, relative inequalities with the general Czech population did not change significantly. Discussion and Conclusions People hospitalised for AUD have much higher mortality rates, resulting in markedly reduced life expectancy. During the pandemic, their mortality rates increased even more. However, the increase was no greater than in the general Czech population.
Changes in stigma and population mental health literacy before and after the Covid-19 pandemic: analyses of repeated cross-sectional studies
The Covid-19 pandemic and related social restrictions have been associated with increased rates of mental health problems, prompting a global surge in interest in mental well-being, which might …
The Covid-19 pandemic and related social restrictions have been associated with increased rates of mental health problems, prompting a global surge in interest in mental well-being, which might have had a positive effect on population mental health literacy (MHL). We aimed to compare levels of mental health related stigma among the Czech general adult population before and after the Covid-19 pandemic, as well as recognition of own mental health problems, among those members of the general population who screened positively for mental disorders. Methods: We conducted a comprehensive analysis of multiple almost identically designed cross-sectional surveys carried out on representative samples of the non-institutionalized adult population in Czechia in 2017, 2019, and 2022. Mental health problems were assessed using the Mini International Neuropsychiatric Interview (M.I.N.I.) in 2017 and 2022, while Self-identification of Mental Illness Scale (SELF-I) gauged self-recognition in 2017 and 2022. Mental health-related stigma was evaluated using the Reported and Intended Behaviour Scale (RIBS) and the Community Attitudes towards Mental Illness scale (CAMI) in 2019 and 2022. Results: Attitudes towards individuals with mental health problems exhibited no statistically significant change; however, reported and intended behaviours, i.e. proxies of social distance, changed for the better. Also, self-recognition of mental health problems demonstrated statistically significant improvements among those screening positive for depression, anxiety, and suicide risk, but not among alcohol use disorders. Conclusions: Population MHL remains low and recent positive changes are likely more attributable to the Covid-19 pandemic and related increase in interest in mental health than to deliberate efforts by government or state or other entities. This underscores the complex interplay between societal factors and mental health outcomes, warranting further exploration and reconsideration of public mental health strategies.
Empowerment or Pressure? Exploring the Impact of Female Body Depictions in Body Positivity Instagram Posts on Self-Objectification
Although Body Positivity content (BoPo) has been criticized for emphasizing physical appearance and promoting self-objectification, the specific features driving these effects remain unclear. The pres…
Although Body Positivity content (BoPo) has been criticized for emphasizing physical appearance and promoting self-objectification, the specific features driving these effects remain unclear. The present study examined whether depictions of female bodies act as triggers for self-objectification in BoPo on Instagram. In a between-subjects online experiment involving 158 women aged 18-29 (M = 21.6, SD = 2.4), exposure to female bodies in BoPo posts did not heighten state self-objectification. Trait self-objectification and negative mood did not moderate these effects; however, women with negative attitudes toward BoPo reported higher levels of state self-objectification. These findings underscore the potential importance of subjective appraisals in shaping the impacts of BoPo content. Overall, the study contributes to the ongoing debate about the potentially negative effects of BoPo on Instagram, suggesting that body depictions alone may not reinforce self-objectification. Future research should examine the distinct influence of different types of body portrayals to further clarify the impact of BoPo content on body image. From a practical perspective, prevention efforts and social media campaigns should aim to raise awareness of BoPo features that continue to overemphasize appearance, helping women better protect their body image from potential adverse effects.
Public Datasets from the Ecological Momentary Assessment Technical Pilot Study Conducted Among University Students (WP1.3, EMA1)
This repository contains data collected for Work Package 1.3: Short-term impacts of ICT use on adult wellbeing, part of the DigiWELL project (Research of Excellence on Digital Technologies and Wellbei…
This repository contains data collected for Work Package 1.3: Short-term impacts of ICT use on adult wellbeing, part of the DigiWELL project (Research of Excellence on Digital Technologies and Wellbeing, CZ.02.01.01/00/22_008/0004583). This study was conducted as a technical pilot (EMA1) to verify the functionality of the research applications used for subsequent EMA studies: Health React (for survey collection) and eBehave (for passive smartphone trace data collection), both developed by the University of Hradec Králové. The seven-day EMA study involved 58 Masaryk University students (58% female; age range: 19–30 years; M = 21.5; SD = 2.3). KeywordsEcological Momentary Assessment, Experience Sampling Method, Technical pilot For additional information or questions, please contact the contact person: Martin Tancoš (tancos@fss.muni.cz).
Trends in Sexual Initiation and Contraception Use Among Czech Adolescents between 2002-2022
Objectives: This study examined trends in sexual behaviour and the timing of sexual initiation among 15-year-old adolescents in Czechia between 2002 and 2022, with a focus on the age of sexual debut (…
Objectives: This study examined trends in sexual behaviour and the timing of sexual initiation among 15-year-old adolescents in Czechia between 2002 and 2022, with a focus on the age of sexual debut (i.e., 15 and older; early at 14; very early at 13 or younger). It also investigated trends in condom and hormonal contraceptive use at most recent intercourse. Methods: Data were drawn from six nationally representative waves of the Czech Health Behaviour in School-aged Children study, which was conducted between 2002 and 2022. Only 15-year-olds were included (N = 19,384). Descriptive trend analyses were conducted using survey weights, with subgroup comparisons by gender and age at sexual initiation. Results: The findings indicate a shift toward later sexual initiation, particularly among girls, with increasing proportions initiating at age 15 or older and declining initiation at age 14. A significant gender gap emerged in 2022, with fewer girls (13.9%) than boys (18.7%) reporting a sexual experience. The prevalence of very early initiation (age 13 or younger) remained stable over time, yet this group—especially boys—consistently accounted for a substantial minority of sexually active adolescents. Condom use declined from 81.2% to 69.9% across all initiation groups between 2014 and 2022, with the most persistent decline among very early initiators. Conclusions: The findings suggest a modest postponement of sexual debut among Czech adolescents and highlight a growing gender disparity in sexual activity by age 15. Persistent early initiation and declining condom use highlight the need for differentiated sexual health education strategies, particularly for younger initiators.
Data for "Trends in adolescent cigarette smoking in Czechia: findings from the HBSC study 2014–2022"
This dataset contains aggregated prevalence estimates regarding cigarette smoking among adolescents in the Czech Republic, derived from three waves of the Health Behaviour in School-aged Children (HBS…
This dataset contains aggregated prevalence estimates regarding cigarette smoking among adolescents in the Czech Republic, derived from three waves of the Health Behaviour in School-aged Children (HBSC) study conducted in 2014, 2018, and 2022. The data represents a total sample of 29,525 respondents (14,761 boys and 14,764 girls) across three target age groups: 11, 13, and 15 years old.
Data for "Trends in alcohol use among Czech adolescents: findings from the HBSC study 2014–2022"
This dataset contains aggregated prevalence estimates regarding alcohol consumption among adolescents in the Czech Republic, based on three waves of the Health Behaviour in School-aged Children (HBSC)…
This dataset contains aggregated prevalence estimates regarding alcohol consumption among adolescents in the Czech Republic, based on three waves of the Health Behaviour in School-aged Children (HBSC) study conducted in 2014, 2018, and 2022. The data represents a total sample of 29,525 respondents (14,761 boys and 13,764 girls) across three target age groups: 11, 13, and 15 years old.
Sensory processing sensitivity and its associations with guilt, shame, self-esteem, and neuroticism
Abstract Background Sensory Processing Sensitivity (SPS) is a trait linked to deeper processing of stimuli and heightened emotional reactivity. These characteristics suggest a potential link to more …
Abstract Background Sensory Processing Sensitivity (SPS) is a trait linked to deeper processing of stimuli and heightened emotional reactivity. These characteristics suggest a potential link to more intense self-conscious emotions. The objective of this study was to investigate the associations between SPS, feelings of guilt, shame, and self-esteem, and to test whether these relationships are independent of the influence of neuroticism. Methods We conducted a cross-sectional study using data from an online survey of Czech adults (n = 1012; 49.3 ± 16.7 years, 50.4% female). Participants completed measures of SPS (Sensory Processing Sensitivity Questionnaire, SPSQ; Highly Sensitive Person Scale), feelings of guilt and shame (Guilt and Shame Experience Scale), self-esteem (Rosenberg Self-Esteem Scale), and neuroticism (the neuroticism subscale of the Big Five Inventory). The associations were examined using linear and logistic regression models, with adjustments for neuroticism and key demographic variables. Results Linear regression analyses showed that higher SPS was significantly associated with increased feelings of guilt and shame, and with lower self-esteem. After adjustment for neuroticism, the association between SPS and self-esteem was no longer significant (β ≈ 0.03, p > 0.05), whereas the β-coefficients for feelings of guilt and shame were reduced but remained significant. Logistic regression analyses comparing low, medium, and high SPS groups and, separately, the equivalent levels on the Sensory Sensitivity subscale of the SPSQ, indicated that highly sensitive individuals were more likely to report feelings of guilt alone and combined guilt and shame, with odds ratios (ORs) ranging from 4.42 (95% CI 2.17–8.99, p < 0.001) to 6.38 (95% CI 3.08–13.25, p < 0.001). No significant associations emerged between SPS and feelings of shame alone or low self-esteem. Analyses using alternative SPS measures yielded broadly similar results, with the Highly Sensitive Person Scale showing even stronger associations with feelings of guilt and shame, while again no effect was found for self-esteem. Conclusions Highly sensitive individuals appear to be more prone to experiencing heightened feelings of guilt and, to a lesser degree, shame. However, the initially observed negative association between SPS and self-esteem was no longer significant after neuroticism was included in the model.
Trends in alcohol use among Czech adolescents: findings from the HBSC study 2014–2022
Objectives: The present study aims to examine trends in adolescent alcohol use over the period from 2014 to 2022.Methods: Data from the last three Health Behaviour in School-aged Children (H…
Objectives: The present study aims to examine trends in adolescent alcohol use over the period from 2014 to 2022.Methods: Data from the last three Health Behaviour in School-aged Children (HBSC) surveys conducted in 2014, 2018 and 2022 were used for this study. Three measures of adolescent alcohol use have been chosen for analyses: lifetime alcohol use, last 30 days alcohol use, and repeated lifetime drunkenness. The analyses comprised calculation of period-specific prevalence estimates and testing of the significance of between-period changes using survey-adjusted logistic regression models.Results: Comparing prevalence rates between the periods, consistent decrease in adolescent alcohol use becomes apparent, particularly for drop of rates in 2018 compared to those in 2014. The corresponding data on the prevalence of lifetime alcohol use among 13-year-old boys was 59.7% in 2014 and 44.2% in 2018; and among 15-year-old boys 80.4% in 2014 and 74.9% in 2018. For 13-year-old girls, the estimated prevalence was 46.9% in 2014 and 41.1% in 2018; and for 15-year-old girls 83.7% in 2014 and 75.9% in 2018. This is the case for repeated lifetime drunkenness, and the decrease is consistent across boys and girls, as well as the respective age groups. In survey waves 2018 and 2022, we do not see a statistically significant decline, but rather a stabilisation of assessed prevalence at a level from the previous wave of the study.Conclusions: The decline in alcohol use among Czech adolescents is part of a global trend of reducing alcohol drinking among young people, on the background of social mechanisms including the change of cultural status of alcohol and changes in young people's leisure preferences.
Trends in adolescent cigarette smoking in Czechia: findings from the HBSC study 2014–2022
Objectives: Regular monitoring of health-related behaviours among vulnerable populations is of public health importance. This study examines recent trends in adolescent cigarette smoking in Czech…
Objectives: Regular monitoring of health-related behaviours among vulnerable populations is of public health importance. This study examines recent trends in adolescent cigarette smoking in Czechia following the marked changes reported in the mid-2010s.Methods: Data from three recent rounds of the Health Behaviour in School-aged Children (HBSC) study conducted in Czechia in 2014, 2018 and 2022 were analysed. Temporal trends were assessed for two indicators of adolescent cigarette use: lifetime cigarette use and cigarette use in the last 30 days. Survey-adjusted binary logistic regression models were used to test changes between survey periods. In 2022, the prevalence of electronic cigarette use was additionally estimated using the same indicators.Results: A continued decline in adolescent cigarette use was observed for both indicators, extending the downward trends reported in the mid-2010s into the 2020s. The decline was most pronounced between 2014 and 2018, with smaller but persistent decreases thereafter, particularly among older adolescents. However, the findings also highlight the substantial prevalence of electronic cigarette use. In 2022, more than one-third of 15-year-olds in Czechia reported lifetime electronic cigarette use (35.1% among boys and 36.6% among girls), and approximately one in five reported use in the last 30 days (19.6% among boys and 23.0% among girls).Conclusions: While conventional cigarette use among adolescents continues to decline, electronic cigarette use represents an important component of contemporary adolescent smoking-related behaviour. In the long term, the phenomenon of electronic cigarettes may counteract intended trends in nicotine-related harms. These findings underscore the need for continued surveillance and prevention efforts in Czechia that address both conventional and emerging smoking-related products.
Životní styl a pohybové chování rodin s 3-8letými dětmi
The long-term research focus on the analysis of the lifestyle of families with prepubertal children and the dismal state of the increase in overweight and obesity in Czech adolescents led the authors …
The long-term research focus on the analysis of the lifestyle of families with prepubertal children and the dismal state of the increase in overweight and obesity in Czech adolescents led the authors to conduct research with families with children aged 3-8 years with the intention of uncovering patterns of all-day movement behaviour, including sleep and physical activity (PA), and formulating understandable recommendations aimed a tanchoring their healthy lifestyle. The presented monograph is methodologically based on a validated instrumental 24-hour monitoring using an accelerometer placed on the wrist of the non-dominant hand capable of detecting even the slightest human movement. A total of 396 families with at least one child aged 3-8 years completed this multiday monitoring of movement behavior.
TRENDS IN THE PERCEPTION OF SCHOOL CLIMATE: HBSC STUDY IN THE CZECH REPUBLIC 1994–2022
Objective: This study aimed to examine long-term trends in Czech adolescents’ perceptions of school climate between 1994 and 2022, focusing on school satisfaction, perceived school pressure, and…
Objective: This study aimed to examine long-term trends in Czech adolescents’ perceptions of school climate between 1994 and 2022, focusing on school satisfaction, perceived school pressure, and perceived social support from classmates and teachers. Methods: Data were drawn from eight cycles of the Czech Health Behaviour in School-aged Children (HBSC) study, encompassing responses from 63,252 students aged 11, 13, and 15. Binary logistic regression analyses were conducted to assess temporal trends and associations between school climate indicators and demographic variables, including gender, age, and family affluence. Results: Findings revealed a pronounced decline in school satisfaction and a significant increase in perceived school pressure, with 2022 showing the lowest satisfaction and highest pressure levels. Perceived support from classmates and teachers declined, especially among students in older age groups and among girls. Socioeconomic disparities had a modest but consistent impact, with students from more affluent backgrounds reporting more favorable school experiences. Conclusions: Czech adolescents’ school climate perceptions have deteriorated over the past three decades, marked by rising demands and weakening support at school. These trends may contribute to reduced school engagement and heightened psychological distress, emphasizing the need for systemic interventions that ensure that high school expectations are coupled with relational and emotional support in schools.
Trends in alcohol use among Czech adolescents, 2014–2022: Findings from the HBSC study
Objectives The present study aims to examining trends in adolescent alcohol use over the period from 2014 to 2022. Methods Data from the last three HBSC surveys conducted in 2014, 2018, and 2022 were …
Objectives The present study aims to examining trends in adolescent alcohol use over the period from 2014 to 2022. Methods Data from the last three HBSC surveys conducted in 2014, 2018, and 2022 were used for this study. Three measures of adolescent alcohol use have been chosen for analyses: lifetime alcohol use, last 30 days alcohol use, and repeated lifetime drunkenness. The analyses comprised calculation of period specific prevalence estimates and testing of the significance of between-period changes using survey-adjusted logistic regression models. Results Comparing prevalence rates between the periods, consistent decrease in adolescent alcohol use becomes apparent, particularly for drop of rates in 2018 compared to those in 2014. This is the case for lifetime alcohol use and repeated lifetime drunkenness, and is consistent across boys and girls, as well as the respective age groups. In survey waves 2018 and 2022, we do not see a statistically significant decline, but rather a stabilisation of assessed prevalence at a level from the previous wave of the study. Conclusions The decline in alcohol use among Czech adolescents is part of a global trend of reducing alcohol drinking among young people, on the background of social mechanisms including the change of cultural status of alcohol and changes in young people's leisure preferences.
Trends in sleep patterns among Czech adolescents and their current correlates of late bedtimes and social jet lag: HBSC study 2014-2022
Objectives: Sleep is vital for maintaining the health and wellbeing of people of all ages. However, for adolescents, sufficient sleep of adequate duration and quality is critical for profound mental, …
Objectives: Sleep is vital for maintaining the health and wellbeing of people of all ages. However, for adolescents, sufficient sleep of adequate duration and quality is critical for profound mental, physical, social, and emotional development. This study aimed to describe trends in sleep duration and late bedtime during school and non-school days in representative cohorts of 11-, 13-, and 15-year-old adolescents from Czechia from 2014 to 2022, and to examine the current associations between late bedtimes/social jet lag and wellbeing indicators among adolescents in 2022. Methods: The analysed sample of 42,101 adolescents aged 10.5-16.5 years was drawn from three nationally representative cohorts of Czech schoolchildren from the last three cycles of the Health Behaviour in School-aged Children study, conducted between 2014 and 2022. Results: Mean sleep duration (hours:minutes) on school and non-school days significantly (p < 0.05) decreased for both boys (schooldays: 8:192014→7:592022; non-school days: 9:362018→9:232022) and girls (schooldays: 8:202014→7:552022; non-school days: 9:582018→9:412022) between 2014/2018 and 2022, while the prevalence of insufficient sleep significantly (p < 0.001) increased over the same period (boys schooldays: 35.4%2014→49.2%2022, boys non-school days: 14.9%2018→18.0%2022; girls schooldays: 35.1%2014→51.7%2022, girls non-school days: 9.8%2018→13.3%2022). Adolescents with late bedtimes or social jet lag (> 2 hours) had significantly higher odds (p < 0.001) of skipping breakfast daily, drinking energy drinks daily, being drunk at least twice in their lifetime, experiencing reduced psychological wellbeing and low life satisfaction, reporting irritability, and problematic social media use and internet gaming than those with earlier bedtimes or without social jet lag. Conclusions: It is highly desirable that families, in close cooperation with schools and professional representatives, make efforts to ensure adherence to the recommended length and quality of sleep, as the trend results indicate worsening sleep patterns, deepening social jet lag, and a disturbing increase in adolescent risk behaviours and health complaints related to insufficient sleep.
PLANETARY-HEALTH LITERACY AND MENTAL WELLBEING IN CZECH ADOLESCENTS: INSIGHTS FROM THE HBSC SURVEY 2022
Objectives: Planetary-health literacy (PHL), the knowledge, motivation and social support required to safeguard both human and environmental health, may help adolescents cope with climate-related dist…
Objectives: Planetary-health literacy (PHL), the knowledge, motivation and social support required to safeguard both human and environmental health, may help adolescents cope with climate-related distress and adopt sustainable behaviours. Evidence on the linkage between PHL and mental health from Central and Eastern Europe is lacking. The aim of the study was to describe PHL in Czech adolescents by sex, grade and family affluence, examine its association with mental-health indicators, and explore links with selected environment-relevant behaviours. Methods: Cross-sectional data were drawn from the nationally representative Health Behaviour in School-aged Children (HBSC) 2022 survey (n = 4,195, 50.8% boys, ages 13 and 15 years). PHL was measured with an 11-item HBSC optional package yielding three sub-scales (knowledge, action, perceived pro-environmental social norms). Outcomes were wellbeing (WHO-5), life satisfaction (Cantril’s ladder), and psychological complaints (HBSC symptom checklist). Fruit and vegetable intake plus cigarette and e-cigarette use served as behavioural correlates. Results: Girls scored higher than boys on all PHL domains (Cohen d = 0.10–0.19). Thirteen-year-olds reported more action and stronger social norms than fifteen-year-olds (p < 0.001); socioeconomic gradients were small. In fully adjusted models, social norms were positively associated with wellbeing (β = 1.42, 95% CI: 1.12–1.72) and life satisfaction (β = 0.10, 0.08–0.13), and inversely with psychological complaints (β = −0.27, −0.33 to −0.21). Knowledge showed weak adverse relations with wellbeing and complaints, whereas action was associated with wellbeing only. Higher PHL related to daily fruit and vegetable consumption and inversely to intensive e-cigarette use; effect sizes were modest. Conclusions: Perceived pro-environmental social norms appear most tightly related to adolescent mental health, while overall PHL is slightly associated with sustainable dietary patterns and lower use of e-cigarettes. School curricula that combine climate education with collaborative, action-oriented projects may therefore deliver co-benefits for planetary and psychological health in Central and Eastern Europe.
